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Merge pull request #46 from HongyiZhouCN/mujoco_binding

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Adding ProDMP support and Box pushing
This commit is contained in:
ottofabian
2022-11-25 14:37:44 +01:00
committed by GitHub
parent ca5800fa3d 833f0735ea
commit 42863cee1c
54 changed files with 1865 additions and 61 deletions
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fancy_gym/black_box/black_box_wrapper.py
+31 -5
View File
@@ -21,7 +21,9 @@ class BlackBoxWrapper(gym.ObservationWrapper):
learn_sub_trajectories: bool = False, learn_sub_trajectories: bool = False,
replanning_schedule: Optional[ replanning_schedule: Optional[
Callable[[np.ndarray, np.ndarray, np.ndarray, np.ndarray, int], bool]] = None, Callable[[np.ndarray, np.ndarray, np.ndarray, np.ndarray, int], bool]] = None,
reward_aggregation: Callable[[np.ndarray], float] = np.sum reward_aggregation: Callable[[np.ndarray], float] = np.sum,
max_planning_times: int = None,
condition_on_desired: bool = False
): ):
""" """
gym.Wrapper for leveraging a black box approach with a trajectory generator. gym.Wrapper for leveraging a black box approach with a trajectory generator.
@@ -60,12 +62,21 @@ class BlackBoxWrapper(gym.ObservationWrapper):
self.return_context_observation = not (learn_sub_trajectories or self.do_replanning) self.return_context_observation = not (learn_sub_trajectories or self.do_replanning)
self.traj_gen_action_space = self._get_traj_gen_action_space() self.traj_gen_action_space = self._get_traj_gen_action_space()
self.action_space = self._get_action_space() self.action_space = self._get_action_space()
self.observation_space = self._get_observation_space() self.observation_space = self._get_observation_space()
# rendering # rendering
self.render_kwargs = {} self.render_kwargs = {}
self.verbose = verbose self.verbose = verbose
# condition value
self.condition_on_desired = condition_on_desired
self.condition_pos = None
self.condition_vel = None
self.max_planning_times = max_planning_times
self.plan_steps = 0
def observation(self, observation): def observation(self, observation):
# return context space if we are # return context space if we are
if self.return_context_observation: if self.return_context_observation:
@@ -86,7 +97,11 @@ class BlackBoxWrapper(gym.ObservationWrapper):
bc_time = np.array(0 if not self.do_replanning else self.current_traj_steps * self.dt) bc_time = np.array(0 if not self.do_replanning else self.current_traj_steps * self.dt)
# TODO we could think about initializing with the previous desired value in order to have a smooth transition # TODO we could think about initializing with the previous desired value in order to have a smooth transition
# at least from the planning point of view. # at least from the planning point of view.
self.traj_gen.set_boundary_conditions(bc_time, self.current_pos, self.current_vel)
condition_pos = self.condition_pos if self.condition_pos is not None else self.current_pos
condition_vel = self.condition_vel if self.condition_vel is not None else self.current_vel
self.traj_gen.set_boundary_conditions(bc_time, condition_pos, condition_vel)
self.traj_gen.set_duration(duration, self.dt) self.traj_gen.set_duration(duration, self.dt)
# traj_dict = self.traj_gen.get_trajs(get_pos=True, get_vel=True) # traj_dict = self.traj_gen.get_trajs(get_pos=True, get_vel=True)
position = get_numpy(self.traj_gen.get_traj_pos()) position = get_numpy(self.traj_gen.get_traj_pos())
@@ -144,8 +159,11 @@ class BlackBoxWrapper(gym.ObservationWrapper):
infos = dict() infos = dict()
done = False done = False
self.plan_steps += 1
for t, (pos, vel) in enumerate(zip(position, velocity)): for t, (pos, vel) in enumerate(zip(position, velocity)):
step_action = self.tracking_controller.get_action(pos, vel, self.current_pos, self.current_vel) current_pos = self.current_pos
current_vel = self.current_vel
step_action = self.tracking_controller.get_action(pos, vel, current_pos, current_vel)
c_action = np.clip(step_action, self.env.action_space.low, self.env.action_space.high) c_action = np.clip(step_action, self.env.action_space.low, self.env.action_space.high)
obs, c_reward, done, info = self.env.step(c_action) obs, c_reward, done, info = self.env.step(c_action)
rewards[t] = c_reward rewards[t] = c_reward
@@ -162,11 +180,18 @@ class BlackBoxWrapper(gym.ObservationWrapper):
if self.render_kwargs: if self.render_kwargs:
self.env.render(**self.render_kwargs) self.env.render(**self.render_kwargs)
if done or self.replanning_schedule(self.current_pos, self.current_vel, obs, c_action, if done or self.replanning_schedule(current_pos, current_vel, obs, c_action,
t + 1 + self.current_traj_steps): t + 1 + self.current_traj_steps):
if self.max_planning_times is not None and self.plan_steps >= self.max_planning_times:
continue
self.condition_pos = pos if self.condition_on_desired else None
self.condition_vel = vel if self.condition_on_desired else None
break break
infos.update({k: v[:t] for k, v in infos.items()}) infos.update({k: v[:t+1] for k, v in infos.items()})
self.current_traj_steps += t + 1 self.current_traj_steps += t + 1
if self.verbose >= 2: if self.verbose >= 2:
@@ -187,5 +212,6 @@ class BlackBoxWrapper(gym.ObservationWrapper):
def reset(self, *, seed: Optional[int] = None, return_info: bool = False, options: Optional[dict] = None): def reset(self, *, seed: Optional[int] = None, return_info: bool = False, options: Optional[dict] = None):
self.current_traj_steps = 0 self.current_traj_steps = 0
self.plan_steps = 0
self.traj_gen.reset() self.traj_gen.reset()
return super(BlackBoxWrapper, self).reset() return super(BlackBoxWrapper, self).reset()
fancy_gym/black_box/factory/basis_generator_factory.py
+6 -1
View File
@@ -1,4 +1,5 @@
from mp_pytorch.basis_gn import NormalizedRBFBasisGenerator, ZeroPaddingNormalizedRBFBasisGenerator from mp_pytorch.basis_gn import NormalizedRBFBasisGenerator, ZeroPaddingNormalizedRBFBasisGenerator, \
ProDMPBasisGenerator
from mp_pytorch.phase_gn import PhaseGenerator from mp_pytorch.phase_gn import PhaseGenerator
ALL_TYPES = ["rbf", "zero_rbf", "rhythmic"] ALL_TYPES = ["rbf", "zero_rbf", "rhythmic"]
@@ -10,6 +11,10 @@ def get_basis_generator(basis_generator_type: str, phase_generator: PhaseGenerat
return NormalizedRBFBasisGenerator(phase_generator, **kwargs) return NormalizedRBFBasisGenerator(phase_generator, **kwargs)
elif basis_generator_type == "zero_rbf": elif basis_generator_type == "zero_rbf":
return ZeroPaddingNormalizedRBFBasisGenerator(phase_generator, **kwargs) return ZeroPaddingNormalizedRBFBasisGenerator(phase_generator, **kwargs)
elif basis_generator_type == "prodmp":
from mp_pytorch.phase_gn import ExpDecayPhaseGenerator
assert isinstance(phase_generator, ExpDecayPhaseGenerator)
return ProDMPBasisGenerator(phase_generator, **kwargs)
elif basis_generator_type == "rhythmic": elif basis_generator_type == "rhythmic":
raise NotImplementedError() raise NotImplementedError()
# return RhythmicBasisGenerator(phase_generator, **kwargs) # return RhythmicBasisGenerator(phase_generator, **kwargs)
fancy_gym/dmc/__init__.py
+1 -1
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@@ -2,7 +2,7 @@ from copy import deepcopy
from . import manipulation, suite from . import manipulation, suite
ALL_DMC_MOVEMENT_PRIMITIVE_ENVIRONMENTS = {"DMP": [], "ProMP": []} ALL_DMC_MOVEMENT_PRIMITIVE_ENVIRONMENTS = {"DMP": [], "ProMP": [], "ProDMP": []}
from gym.envs.registration import register from gym.envs.registration import register
fancy_gym/envs/__init__.py
+80 -5
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@@ -16,8 +16,10 @@ from .mujoco.hopper_throw.hopper_throw import MAX_EPISODE_STEPS_HOPPERTHROW
from .mujoco.hopper_throw.hopper_throw_in_basket import MAX_EPISODE_STEPS_HOPPERTHROWINBASKET from .mujoco.hopper_throw.hopper_throw_in_basket import MAX_EPISODE_STEPS_HOPPERTHROWINBASKET
from .mujoco.reacher.reacher import ReacherEnv, MAX_EPISODE_STEPS_REACHER from .mujoco.reacher.reacher import ReacherEnv, MAX_EPISODE_STEPS_REACHER
from .mujoco.walker_2d_jump.walker_2d_jump import MAX_EPISODE_STEPS_WALKERJUMP from .mujoco.walker_2d_jump.walker_2d_jump import MAX_EPISODE_STEPS_WALKERJUMP
from .mujoco.box_pushing.box_pushing_env import BoxPushingDense, BoxPushingTemporalSparse, \
BoxPushingTemporalSpatialSparse, MAX_EPISODE_STEPS_BOX_PUSHING
ALL_FANCY_MOVEMENT_PRIMITIVE_ENVIRONMENTS = {"DMP": [], "ProMP": []} ALL_FANCY_MOVEMENT_PRIMITIVE_ENVIRONMENTS = {"DMP": [], "ProMP": [], "ProDMP": []}
DEFAULT_BB_DICT_ProMP = { DEFAULT_BB_DICT_ProMP = {
"name": 'EnvName', "name": 'EnvName',
@@ -36,7 +38,8 @@ DEFAULT_BB_DICT_ProMP = {
"basis_generator_kwargs": { "basis_generator_kwargs": {
'basis_generator_type': 'zero_rbf', 'basis_generator_type': 'zero_rbf',
'num_basis': 5, 'num_basis': 5,
'num_basis_zero_start': 1 'num_basis_zero_start': 1,
'basis_bandwidth_factor': 3.0,
} }
} }
@@ -60,6 +63,29 @@ DEFAULT_BB_DICT_DMP = {
} }
} }
DEFAULT_BB_DICT_ProDMP = {
"name": 'EnvName',
"wrappers": [],
"trajectory_generator_kwargs": {
'trajectory_generator_type': 'prodmp',
},
"phase_generator_kwargs": {
'phase_generator_type': 'exp',
},
"controller_kwargs": {
'controller_type': 'motor',
"p_gains": 1.0,
"d_gains": 0.1,
},
"basis_generator_kwargs": {
'basis_generator_type': 'prodmp',
'alpha': 10,
'num_basis': 5,
},
"black_box_kwargs": {
}
}
# Classic Control # Classic Control
## Simple Reacher ## Simple Reacher
register( register(
@@ -197,6 +223,14 @@ register(
max_episode_steps=MAX_EPISODE_STEPS_BEERPONG, max_episode_steps=MAX_EPISODE_STEPS_BEERPONG,
) )
# Box pushing environments with different rewards
for reward_type in ["Dense", "TemporalSparse", "TemporalSpatialSparse"]:
register(
id='BoxPushing{}-v0'.format(reward_type),
entry_point='fancy_gym.envs.mujoco:BoxPushing{}'.format(reward_type),
max_episode_steps=MAX_EPISODE_STEPS_BOX_PUSHING,
)
# Here we use the same reward as in BeerPong-v0, but now consider after the release, # Here we use the same reward as in BeerPong-v0, but now consider after the release,
# only one time step, i.e. we simulate until the end of th episode # only one time step, i.e. we simulate until the end of th episode
register( register(
@@ -205,7 +239,6 @@ register(
max_episode_steps=FIXED_RELEASE_STEP, max_episode_steps=FIXED_RELEASE_STEP,
) )
# movement Primitive Environments # movement Primitive Environments
## Simple Reacher ## Simple Reacher
@@ -326,7 +359,6 @@ for _v in _versions:
kwargs=kwargs_dict_reacher_promp kwargs=kwargs_dict_reacher_promp
) )
ALL_FANCY_MOVEMENT_PRIMITIVE_ENVIRONMENTS["ProMP"].append(_env_id) ALL_FANCY_MOVEMENT_PRIMITIVE_ENVIRONMENTS["ProMP"].append(_env_id)
######################################################################################################################## ########################################################################################################################
## Beerpong ProMP ## Beerpong ProMP
_versions = ['BeerPong-v0'] _versions = ['BeerPong-v0']
@@ -431,7 +463,50 @@ for _v in _versions:
ALL_FANCY_MOVEMENT_PRIMITIVE_ENVIRONMENTS["ProMP"].append(_env_id) ALL_FANCY_MOVEMENT_PRIMITIVE_ENVIRONMENTS["ProMP"].append(_env_id)
# ######################################################################################################################## # ########################################################################################################################
#
## Box Pushing
_versions = ['BoxPushingDense-v0', 'BoxPushingTemporalSparse-v0', 'BoxPushingTemporalSpatialSparse-v0']
for _v in _versions:
_name = _v.split("-")
_env_id = f'{_name[0]}ProMP-{_name[1]}'
kwargs_dict_box_pushing_promp = deepcopy(DEFAULT_BB_DICT_ProMP)
kwargs_dict_box_pushing_promp['wrappers'].append(mujoco.box_pushing.MPWrapper)
kwargs_dict_box_pushing_promp['name'] = _v
kwargs_dict_box_pushing_promp['controller_kwargs']['p_gains'] = 0.01 * np.array([120., 120., 120., 120., 50., 30., 10.])
kwargs_dict_box_pushing_promp['controller_kwargs']['d_gains'] = 0.01 * np.array([10., 10., 10., 10., 6., 5., 3.])
kwargs_dict_box_pushing_promp['basis_generator_kwargs']['basis_bandwidth_factor'] = 2 # 3.5, 4 to try
register(
id=_env_id,
entry_point='fancy_gym.utils.make_env_helpers:make_bb_env_helper',
kwargs=kwargs_dict_box_pushing_promp
)
ALL_FANCY_MOVEMENT_PRIMITIVE_ENVIRONMENTS["ProMP"].append(_env_id)
for _v in _versions:
_name = _v.split("-")
_env_id = f'{_name[0]}ReplanProDMP-{_name[1]}'
kwargs_dict_box_pushing_prodmp = deepcopy(DEFAULT_BB_DICT_ProDMP)
kwargs_dict_box_pushing_prodmp['wrappers'].append(mujoco.box_pushing.MPWrapper)
kwargs_dict_box_pushing_prodmp['name'] = _v
kwargs_dict_box_pushing_prodmp['controller_kwargs']['p_gains'] = 0.01 * np.array([120., 120., 120., 120., 50., 30., 10.])
kwargs_dict_box_pushing_prodmp['controller_kwargs']['d_gains'] = 0.01 * np.array([10., 10., 10., 10., 6., 5., 3.])
kwargs_dict_box_pushing_prodmp['trajectory_generator_kwargs']['weights_scale'] = 0.3
kwargs_dict_box_pushing_prodmp['trajectory_generator_kwargs']['goal_scale'] = 0.3
kwargs_dict_box_pushing_prodmp['trajectory_generator_kwargs']['auto_scale_basis'] = True
kwargs_dict_box_pushing_prodmp['trajectory_generator_kwargs']['goal_offset'] = 1.0
kwargs_dict_box_pushing_prodmp['basis_generator_kwargs']['num_basis'] = 4
kwargs_dict_box_pushing_prodmp['basis_generator_kwargs']['basis_bandwidth_factor'] = 3
kwargs_dict_box_pushing_prodmp['phase_generator_kwargs']['alpha_phase'] = 3
kwargs_dict_box_pushing_prodmp['black_box_kwargs']['max_planning_times'] = 2
kwargs_dict_box_pushing_prodmp['black_box_kwargs']['replanning_schedule'] = lambda pos, vel, obs, action, t : t % 25 == 0
kwargs_dict_box_pushing_prodmp['black_box_kwargs']['condition_on_desired'] = True
register(
id=_env_id,
entry_point='fancy_gym.utils.make_env_helpers:make_bb_env_helper',
kwargs=kwargs_dict_box_pushing_prodmp
)
ALL_FANCY_MOVEMENT_PRIMITIVE_ENVIRONMENTS["ProDMP"].append(_env_id)
# #
# ## Walker2DJump # ## Walker2DJump
# _versions = ['Walker2DJump-v0'] # _versions = ['Walker2DJump-v0']
fancy_gym/envs/mujoco/__init__.py
+1
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@@ -7,3 +7,4 @@ from .hopper_throw.hopper_throw import HopperThrowEnv
from .hopper_throw.hopper_throw_in_basket import HopperThrowInBasketEnv from .hopper_throw.hopper_throw_in_basket import HopperThrowInBasketEnv
from .reacher.reacher import ReacherEnv from .reacher.reacher import ReacherEnv
from .walker_2d_jump.walker_2d_jump import Walker2dJumpEnv from .walker_2d_jump.walker_2d_jump import Walker2dJumpEnv
from .box_pushing.box_pushing_env import BoxPushingDense, BoxPushingTemporalSparse, BoxPushingTemporalSpatialSparse
fancy_gym/envs/mujoco/box_pushing/__init__.py
+1
View File
@@ -0,0 +1 @@
from .mp_wrapper import MPWrapper
fancy_gym/envs/mujoco/box_pushing/assets/box_pushing.xml
+42
View File
@@ -0,0 +1,42 @@
<mujoco model="base_surrounding">
<compiler angle="radian" discardvisual="false" />
<option collision="all" cone="elliptic" gravity="0 0 -9.81" impratio="3" solver="Newton" timestep="0.002" tolerance="1e-10" />
<size nconmax="2000" njmax="2000" />
<asset>
<texture builtin="gradient" height="32" rgb1="0.26 0.58 0.51" rgb2="0.26 0.58 0.51" type="skybox" width="32" />
</asset>
<visual>
<map zfar="1000" znear="0.001" />
</visual>
<worldbody>
<light castshadow="true" cutoff="100" diffuse="1 1 1" dir="-0 0 -1.3" directional="true" exponent="1" pos="0 0 1.3" specular=".1 .1 .1" />
<body name="ground" pos="0 0 -0.94">
<geom name="ground:geom1" rgba="0.26 0.58 0.51 1" size="0 0 1" type="plane" />
</body>
<!-- <body name="goal" pos="0.5 0 0.2" quat="1 0 0 0">-->
<!-- <geom type="sphere" mass="0.1" size="0.01" rgba="1 0 0 1" />-->
<!-- </body>-->
<!-- <body name="box" pos="0.5 0 0.1" quat="1 0 0 0">-->
<!-- <geom type="box" mass="1" size="0.02 0.02 0.02" rgba="0.32 0.32 0.32 1" />-->
<!-- </body>-->
<body name="target_pos" pos="0.5 0.5 0.0">
<site type="box" pos="0 0 0" rgba="0 1 0 0.3" size="0.05 0.05 0.01" />
<site pos="0.05 0 0.0485" size="0.005 0.05 0.045" rgba="0 0 1 0.5" type="box"/>
<site pos="0 0.05 0.0485" size="0.05 0.005 0.045" rgba="0 1 0 0.5" type="box"/>
<site pos="-0.05 0 0.0485" size="0.005 0.05 0.045" rgba="0 1 0 0.5" type="box"/>
<site pos="0 -0.05 0.0485" size="0.05 0.005 0.045" rgba="0 1 0 0.5" type="box"/>
</body>
<body name="replan_target_pos" pos="0.5 0.5 -0.01">
<site type="box" pos="0 0 0" rgba="1 1 0 0.3" size="0.05 0.05 0.01" />
<site pos="0.05 0 0.0485" size="0.005 0.05 0.045" rgba="0 0 1 0.5" type="box"/>
<site pos="0 0.05 0.0485" size="0.05 0.005 0.045" rgba="1 1 0 0.5" type="box"/>
<site pos="-0.05 0 0.0485" size="0.005 0.05 0.045" rgba="1 1 0 0.5" type="box"/>
<site pos="0 -0.05 0.0485" size="0.05 0.005 0.045" rgba="1 1 0 0.5" type="box"/>
</body>
</worldbody>
<include file="kit_lab_surrounding.xml" />
<include file="panda_rod.xml" />
<include file="push_box.xml" />
</mujoco>
fancy_gym/envs/mujoco/box_pushing/assets/kit_lab_surrounding.xml
+118
View File
@@ -0,0 +1,118 @@
<mujoco model="PandaFrame">
<worldbody>
<body name="table_plane" pos="0.2 0 -0.02">
<geom type="box" size="0.49 0.98 0.001" rgba="0.8 0.655 0.45 1" solimp="0.999 0.999 0.001"
solref="0.002 1"/>
<body name="panda_ground" pos="-0.24 0 0.01">
<geom type="box" size="0.18 0.18 0.01" rgba=".9 .9 .9 1"/>
</body>
<body name="front_upper" pos="0.49 0 -0.02">
<geom type="box" size="0.02 0.96 0.02" rgba=".9 .9 .9 1"/>
</body>
<body name="front_mid" pos="0.49 0 -0.445">
<geom type="box" size="0.02 0.96 0.02" rgba=".9 .9 .9 1"/>
</body>
<body name="front_lower" pos="0.49 0 -0.87">
<geom type="box" size="0.02 0.96 0.02" rgba=".9 .9 .9 1"/>
</body>
<body name="back_upper" pos="-0.49 0 -0.02">
<geom type="box" size="0.02 0.96 0.02" rgba=".9 .9 .9 1"/>
</body>
<body name="back_mid" pos="-0.49 0 -0.445">
<geom type="box" size="0.02 0.96 0.02" rgba=".9 .9 .9 1"/>
</body>
<body name="back_lower" pos="-0.49 0 -0.87">
<geom type="box" size="0.02 0.96 0.02" rgba=".9 .9 .9 1"/>
</body>
<body name="vert_front_left" pos="0.49 0.98 -0.445">
<geom type="box" size="0.02 0.02 0.445" rgba=".9 .9 .9 1"/>
</body>
<body name="vert_front_right" pos="0.49 -0.98 -0.445">
<geom type="box" size="0.02 0.02 0.445" rgba=".9 .9 .9 1"/>
</body>
<body name="vert_back_left" pos="-0.49 0.98 -0.445">
<geom type="box" size="0.02 0.02 0.445" rgba=".9 .9 .9 1"/>
</body>
<body name="vert_back_right" pos="-0.49 -0.98 -0.445">
<geom type="box" size="0.02 0.02 0.445" rgba=".9 .9 .9 1"/>
</body>
<body name="side_upper_right" pos="0 -0.98 -0.02">
<geom type="box" size="0.47 0.02 0.02" rgba=".9 .9 .9 1"/>
</body>
<body name="side_lower_right" pos="0 -0.98 -0.87">
<geom type="box" size="0.47 0.02 0.02" rgba=".9 .9 .9 1"/>
</body>
<body name="side_upper_left" pos="0 0.98 -0.02">
<geom type="box" size="0.47 0.02 0.02" rgba=".9 .9 .9 1"/>
</body>
<body name="side_lower_left" pos="0 0.98 -0.87">
<geom type="box" size="0.47 0.02 0.02" rgba=".9 .9 .9 1"/>
</body>
<body name="foot_front_left" pos="0.49 0.98 -0.9">
<geom type="cylinder" size="0.02 0.02 0.04" rgba="0 0 0 1"/>
</body>
<body name="foot_front_mid" pos="0.49 0 -0.9">
<geom type="cylinder" size="0.02 0.02 0.04" rgba="0 0 0 1"/>
</body>
<body name="foot_front_right" pos="0.49 -0.98 -0.9">
<geom type="cylinder" size="0.02 0.02 0.04" rgba="0 0 0 1"/>
</body>
<body name="foot_back_left" pos="-0.49 0.98 -0.9">
<geom type="cylinder" size="0.02 0.02 0.04" rgba="0 0 0 1"/>
</body>
<body name="foot_back_mid" pos="-0.49 0 -0.9">
<geom type="cylinder" size="0.02 0.02 0.04" rgba="0 0 0 1"/>
</body>
<body name="foot_back_right" pos="-0.49 -0.98 -0.9">
<geom type="cylinder" size="0.02 0.02 0.04" rgba="0 0 0 1"/>
</body>
<body name="top_back_left" pos="-0.49 0.98 0.65">
<geom type="box" size="0.02 0.02 0.65" rgba=".9 .9 .9 1"/>
</body>
<body name="top_back_right" pos="-0.49 -0.98 0.65">
<geom type="box" size="0.02 0.02 0.65" rgba=".9 .9 .9 1"/>
</body>
<body name="top_front" pos="0.49 0 1.28">
<geom type="box" size="0.02 0.96 0.02" rgba=".9 .9 .9 1"/>
</body>
<body name="top_back" pos="-0.49 0 1.28">
<geom type="box" size="0.02 0.96 0.02" rgba=".9 .9 .9 1"/>
</body>
<body name="top_side_right" pos="0 -0.98 1.28">
<geom type="box" size="0.51 0.02 0.02" rgba=".9 .9 .9 1"/>
</body>
<body name="top_side_mid" pos="0 0 1.28">
<geom type="box" size="0.51 0.02 0.02" rgba=".9 .9 .9 1"/>
</body>
<body name="top_side_left" pos="0 0.98 1.28">
<geom type="box" size="0.51 0.02 0.02" rgba=".9 .9 .9 1"/>
</body>
<body name="rot_lower_right" pos="-0.31 0.98 0.22" quat="0.2 0 0.1 0">
<geom type="box" size="0.3 0.02 0.02" rgba=".9 .9 .9 1"/>
</body>
<body name="rot_lower_left" pos="-0.31 -0.98 0.22" quat="0.2 0 0.1 0">
<geom type="box" size="0.3 0.02 0.02" rgba=".9 .9 .9 1"/>
</body>
<body name="rot_upper_right" pos="-0.31 0.98 1.04" quat="0.1 0 0.2 0">
<geom type="box" size="0.3 0.02 0.02" rgba=".9 .9 .9 1"/>
</body>
<body name="rot_upper_left" pos="-0.31 -0.98 1.04" quat="0.1 0 0.2 0">
<geom type="box" size="0.3 0.02 0.02" rgba=".9 .9 .9 1"/>
</body>
<body name="support_body" pos="0 0 -0.4" >
<geom type="box" size="0.49 0.98 0.4" rgba="0 0 0 0" solimp="0.999 0.999 0.001" solref="0.002 1"/>
</body>
</body>
</worldbody>
</mujoco>
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<mujocoinclude>
<compiler angle="radian" discardvisual="false" meshdir="./meshes/panda/"/>
<asset>
<mesh file="link0v.stl" name="link0v"/>
<mesh file="link1v.stl" name="link1v"/>
<mesh file="link2v.stl" name="link2v"/>
<mesh file="link3v.stl" name="link3v"/>
<mesh file="link4v.stl" name="link4v"/>
<mesh file="link5v.stl" name="link5v"/>
<mesh file="link6v.stl" name="link6v"/>
<mesh file="link7v.stl" name="link7v"/>
<mesh file="handv.stl" name="handv"/>
<mesh file="fingerv.stl" name="fingerv"/>
</asset>
<contact>
<exclude body1="panda_link0" body2="panda_link1"/>
<exclude body1="panda_link1" body2="panda_link2"/>
<exclude body1="panda_link2" body2="panda_link3"/>
<exclude body1="panda_link3" body2="panda_link4"/>
<exclude body1="panda_link4" body2="panda_link5"/>
<exclude body1="panda_link5" body2="panda_link6"/>
<exclude body1="panda_link6" body2="panda_link7"/>
<exclude body1="panda_link7" body2="panda_link8"/>
<exclude body1="panda_link8" body2="panda_hand"/>
</contact>
<worldbody>
<body name="panda_link0" pos="0 0 0">
<inertial diaginertia="0.0126801 0.0117603 0.00856656" mass="3.01399" pos="-0.0291898 -0.000879465 0.0566032"
quat="0.00411744 0.564916 0.0132875 0.825031"/>
<geom conaffinity="0" contype="0" group="1" mesh="link0v" name="panda_link0:geom1" rgba="1 1 1 1" type="mesh"/>
<geom mesh="link0v" name="panda_link0:geom2" rgba="1 1 1 1" type="mesh"/>
<body name="panda_link1" pos="0 0 0.333">
<inertial diaginertia="0.0164224 0.0153969 0.00546286" mass="2.77281" pos="1.1399e-05 -0.0312655 -0.0693733"
quat="0.98466 0.174481 -0.000101815 0.000347662"/>
<joint axis="0 0 1" limited="true" name="panda_joint1" pos="0 0 0" range="-2.9671 2.9671"/>
<geom conaffinity="0" contype="0" group="1" mesh="link1v" name="panda_joint1:geom1" rgba="1 1 1 1" type="mesh"/>
<geom mesh="link1v" name="panda_joint1:geom2" rgba="1 1 1 1" type="mesh"/>
<body name="panda_link2" pos="0 0 0" quat="0.707107 -0.707107 0 0">
<inertial diaginertia="0.016787 0.0157415 0.00553027" mass="2.7996" pos="-1.31766e-05 -0.0703216 0.0311782"
quat="0.57484 0.818266 -6.05764e-05 -6.61626e-05"/>
<joint axis="0 0 1" limited="true" name="panda_joint2" pos="0 0 0" range="-1.8326
1.8326"/>
<geom conaffinity="0" contype="0" group="1" mesh="link2v" name="panda_joint2:geom1" rgba="1 1 1 1" type="mesh"/>
<geom mesh="link2v" name="panda_joint2:geom2" rgba="1 1 1 1" type="mesh"/>
<body name="panda_link3" pos="0 -0.316 0" quat="0.707107 0.707107 0 0">
<inertial diaginertia="0.00915257 0.00896477 0.00384742" mass="2.14603" pos="0.0443483 0.0249283 -0.03813"
quat="0.0615263 0.349824 0.234291 0.904956"/>
<joint axis="0 0 1" limited="true" name="panda_joint3" pos="0 0 0" range="-2.9671 2.9671"/>
<geom conaffinity="0" contype="0" group="1" mesh="link3v" name="panda_joint3:geom1" rgba="1 1 1 1" type="mesh"/>
<geom mesh="link3v" name="panda_joint3:geom2" rgba="1 1 1 1" type="mesh"/>
<body name="panda_link4" pos="0.0825 0 0" quat="0.707107 0.707107 0 0">
<inertial diaginertia="0.00946899 0.00928491 0.00396694" mass="2.18807" pos="-0.0385503 0.0395256 0.0247162"
quat="0.813566 0.465041 0.309792 0.160858"/>
<joint axis="0 0 1" limited="true" name="panda_joint4" pos="0 0 0" range="-3.1416 0.0873"/>
<geom conaffinity="0" contype="0" group="1" mesh="link4v" name="panda_link4:geom1" rgba="1 1 1 1" type="mesh"/>
<geom mesh="link4v" name="panda_link4:geom2" rgba="1 1 1 1" type="mesh"/>
<body name="panda_link5" pos="-0.0825 0.384 0" quat="0.707107 -0.707107 0 0">
<inertial diaginertia="0.0278873 0.0268823 0.00569569" mass="3.19545" pos="-6.36776e-05 0.0384124 -0.10997"
quat="0.990767 -0.135571 0.000963106 0.000694406"/>
<joint axis="0 0 1" limited="true" name="panda_joint5" pos="0 0 0" range="-2.9671 2.9671"/>
<geom conaffinity="0" contype="0" group="1" mesh="link5v" name="panda_link5:geom1" rgba="1 1 1 1" type="mesh"/>
<geom mesh="link5v" name="panda_link5:geom2" rgba="1 1 1 1" type="mesh"/>
<body name="panda_link6" pos="0 0 0" quat="0.707107 0.707107 0 0">
<inertial diaginertia="0.00412168 0.0033698 0.00213304" mass="1.35761" pos="0.0510023 0.00693267 0.00616899"
quat="-0.0460841 0.754362 0.044494 0.653325"/>
<joint axis="0 0 1" limited="true" name="panda_joint6" pos="0 0 0" range="-0.0873 3.8223"/>
<geom conaffinity="0" contype="0" group="1" mesh="link6v" name="panda_link6:geom1" rgba="1 1 1 1" type="mesh"/>
<geom mesh="link6v" name="panda_link6:geom2" rgba="1 1 1 1" type="mesh"/>
<body name="panda_link7" pos="0.088 0 0" quat="0.707107 0.707107 0 0">
<inertial diaginertia="0.000637671 0.000528056 0.000279577" mass="0.417345"
pos="0.0103614 0.0103596 0.0791078" quat="0.63547 0.278021 -0.670462 0.263369"/>
<joint axis="0 0 1" limited="true" name="panda_joint7" pos="0 0 0" range="-2.9671 2.9671"/>
<geom conaffinity="0" contype="0" group="1" mesh="link7v" name="panda_link7:geom1" rgba="1 1 1 1"
type="mesh"/>
<geom mesh="link7v" name="panda_link7:geom2" rgba="1 1 1 1" solimp="1.998 1.999 0" type="mesh"/>
<body name="panda_link8" pos="0 0 0.107">
<inertial diaginertia="0.1 0.1 0.1" mass="0.1" pos="0 0 0"/>
<body name="panda_hand" pos="0 0 0" quat="0.92388 0 0 -0.382683">
<inertial diaginertia="0.00227632 0.00206087 0.000456542" mass="0.670782"
pos="-2.76618e-06 -0.00061547 0.0239295" quat="0.697945 0.716151 -0.000242485 8.47563e-05"/>
<geom conaffinity="0" contype="0" group="1" mesh="handv" name="panda_hand:geom1" type="mesh"/>
<geom mesh="handv" name="panda_hand:geom2" solimp="1.998 1.999 0" type="mesh"/>
<camera euler="0 3.14 0" fovy="60" ipd="0.0" mode="fixed" name="rgbd" pos="0.1 0.0 0."/>
<body name="tcp" pos="0 0 0.105">
<site name="tcp" rgba='0 0 1 0' size='0.001'/>
</body>
<!-- PUSHROD -->
<body name="push_rod">
<geom type="cylinder" size="0.01 0.15" pos="0 0 0.075"/>
<site name="rod_tip" type="box" rgba="0 0 1 1." size="0.01 0.01 0.01" pos="0 0 0.2"/>
</body>
<body childclass="panda:gripper" name="panda_leftfinger" pos="0 0 0.0584">
<inertial diaginertia="5.69661e-06 5.56035e-06 1.55183e-06" mass="0.0218754"
pos="-2.42335e-05 0.0119585 0.0237816" quat="0.996177 0.0868631 -2.79377e-05 -0.00926642"/>
<joint axis="0 1 0" name="panda_finger_joint1"/>
<geom conaffinity="0" contype="0" group="1" mesh="fingerv" name="panda_leftfinger:geom1" type="mesh"/>
<geom mesh="fingerv" name="panda_leftfinger:geom2" type="mesh"/>
<site name="panda_leftfinger:site" pos="0 0.012 0.045" rgba="0 0 1 0" size="0.012 0.015 0.012" type="box"/>
<body name="finger_joint1_tip" pos="0 0.0085 0.056">
<inertial diaginertia="0.01 0.01 0.01" mass="0.01" pos="0 0 0" quat="0 0 0 1"/>
<geom conaffinity="1" condim="4" contype="1" friction="2 0.05 0.0001" name="finger1_tip_collision"
pos="0 -0.005 -0.012" quat="0 0 0 1" rgba="1 0 0 1" size="0.008 0.004 0.008" solref="0.01 0.5"
type="box"/>
</body>
</body>
<body childclass="panda:gripper" name="panda_rightfinger" pos="0 0 0.0584">
<inertial diaginertia="5.69661e-06 5.56035e-06 1.55183e-06" mass="0.0218754"
pos="2.42335e-05 -0.0119585 0.0237816" quat="0.996177 -0.0868631 2.79377e-05 -0.00926642"/>
<joint axis="0 -1 0" name="panda_finger_joint2"/>
<site name="panda_rightfinger:site" pos="0 -0.012 0.045" rgba="0 0 1 0" size="0.012 0.015 0.012"
type="box"/>
<geom conaffinity="0" contype="0" group="1" mesh="fingerv" name="panda_rightfinger:geom1" quat="0 0 0 1"
type="mesh"/>
<geom condim="4" mesh="fingerv" name="panda_rightfinger:geom2" quat="0 0 0 1" type="mesh"/>
<body name="finger_joint2_tip" pos="0 -0.0085 0.056">
<inertial diaginertia="0.01 0.01 0.01" mass="0.01" pos="0 0 0" quat="0 0 0 1"/>
<geom conaffinity="1" condim="4" contype="1" friction="2 0.05 0.0001" name="finger2_tip_collision"
pos="0 0.005 -0.012" quat="0 0 0 1" rgba="1 0 0 1" size="0.008 0.004 0.008" solref="0.01 0.5"
type="box"/>
</body>
</body>
</body>
</body>
</body>
</body>
</body>
</body>
</body>
</body>
</body>
</body>
<camera euler="0 0 0" fovy="45" ipd="0.0" mode="fixed" name="rgbd_cage" pos="0.7 0.1 0.9"/>
</worldbody>
<sensor>
<touch name="touchsensor:left" site="panda_leftfinger:site"/>
<touch name="touchsensor:right" site="panda_rightfinger:site"/>
</sensor>
<default>
<default class="panda:gripper">
<geom condim="4" friction="1 0.005 0.0001" margin="0.001" solimp="0.998 0.999 0.001" solref="0.02 1" type="box"/>
<joint damping="10" limited="true" range="0 0.04" type="slide"/>
</default>
</default>
<actuator>
<motor forcelimited="true" forcerange="-87 87" joint="panda_joint1" name="panda_joint1_act"/>
<motor forcelimited="true" forcerange="-87 87" joint="panda_joint2" name="panda_joint2_act"/>
<motor forcelimited="true" forcerange="-87 87" joint="panda_joint3" name="panda_joint3_act"/>
<motor forcelimited="true" forcerange="-87 87" joint="panda_joint4" name="panda_joint4_act"/>
<motor forcelimited="true" forcerange="-12 12" joint="panda_joint5" name="panda_joint5_act"/>
<motor forcelimited="true" forcerange="-12 12" joint="panda_joint6" name="panda_joint6_act"/>
<motor forcelimited="true" forcerange="-12 12" joint="panda_joint7" name="panda_joint7_act"/>
<!-- <position ctrllimited="true" ctrlrange="0.001 0.04" forcelimited="true" forcerange="-70 70"-->
<!-- joint="panda_finger_joint1" kp="1000000" name="panda_finger_joint1_act"/>-->
<!-- <position ctrllimited="true" ctrlrange="0.001 0.04" forcelimited="true" forcerange="-70 70"-->
<!-- joint="panda_finger_joint2" kp="1000000" name="panda_finger_joint2_act"/>-->
</actuator>
</mujocoinclude>
fancy_gym/envs/mujoco/box_pushing/assets/push_box.xml
+12
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<mujoco model="push_box">
<worldbody>
<body name="box_0" pos="0.6 0.15 0.0" quat="1 0 0 0">
<geom pos="0 0 0" size="0.05 0.05 0.01" rgba="1 0 0 1.0" type="box" mass="2.0" friction="0.3 0.001 0.0001" priority="1"/>
<geom pos="0.05 0 0.0485" size="0.005 0.05 0.045" rgba="0 0 1 1.0" type="box" mass="0.001"/>
<geom pos="0 0.05 0.0485" size="0.05 0.005 0.045" rgba="1 0 0 1.0" type="box" mass="0.001"/>
<geom pos="-0.05 0 0.0485" size="0.005 0.05 0.045" rgba="1 0 0 1.0" type="box" mass="0.001"/>
<geom pos="0 -0.05 0.0485" size="0.05 0.005 0.045" rgba="1 0 0 1.0" type="box" mass="0.001"/>
<joint name="box_joint" type="free"/>
</body>
</worldbody>
</mujoco>
fancy_gym/envs/mujoco/box_pushing/assets/robots/controller/panda_mocap_control.xml
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<mujocoinclude>
<worldbody>
<body mocap="true" name="panda:mocap" pos="0 0 0">
<geom conaffinity="0" contype="0" pos="0 0 0" rgba="0 0.5 0 0.7" size="0.005 0.005 0.005" type="box" />
<!-- <geom conaffinity="0" contype="0" pos="0 0 0" rgba="1 0 0 0.3" size="1 0.005 0.005" type="box" />-->
<!-- <geom conaffinity="0" contype="0" pos="0 0 0" rgba="0 1 0 0.3" size="0.005 1 0.005" type="box" />-->
<!-- <geom conaffinity="0" contype="0" pos="0 0 0" rgba="0 0 1 0.3" size="0.005 0.005 1" type="box" />-->
</body>
</worldbody>
<equality>
<weld body1="panda:mocap" body2="tcp" solref="0.001 1"/>
</equality>
</mujocoinclude>
fancy_gym/envs/mujoco/box_pushing/assets/robots/controller/panda_position_control.xml
+50
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<mujocoinclude>
<actuator>
<position class="panda" ctrlrange="-2.9671 2.9671" forcerange="-87 87" joint="panda_joint1" kp="8700.0"
name="panda_joint1"/>
<position class="panda" ctrlrange="-1.8326 1.8326" forcerange="-87 87" joint="panda_joint2" kp="8700.0"
name="panda_joint2"/>
<position class="panda" ctrlrange="-2.9671 2.9671" forcerange="-87 87" joint="panda_joint3" kp="8700.0"
name="panda_joint3"/>
<position class="panda" ctrlrange="-3.1416 0.0" forcerange="-87 87" joint="panda_joint4" kp="8700.0"
name="panda_joint4"/>
<position class="panda" ctrlrange="-2.9671 2.9671" forcerange="-12 12" joint="panda_joint5" kp="1200.0"
name="panda_joint5"/>
<position class="panda" ctrlrange="-3.7525 2.1817" forcerange="-12 12" joint="panda_joint6" kp="1200.0"
name="panda_joint6"/>
<position class="panda" ctrlrange="-2.9671 2.9671" forcerange="-12 12" joint="panda_joint7" kp="1200.0"
name="panda_joint7"/>
</actuator>
<default>
<default class="panda">
<joint axis="0 0 1" limited="true" pos="0 0 0"/>
<position ctrllimited="true" forcelimited="true" user="1002 40 2001 -0.005 0.005"/>
<default class="panda_viz">
<geom conaffinity="0" contype="0" group="0" mass="0" rgba=".95 .99 .92 1" type="mesh"/>
</default>
<default class="panda_col">
<geom conaffinity="1" contype="1" group="3" rgba=".5 .6 .7 1" type="mesh"/>
</default>
<default class="panda_arm">
<joint damping="100"/>
</default>
<default class="panda_forearm">
<joint damping="10"/>
</default>
<default class="panda_finger">
<joint armature="5" damping="100"/>
<geom conaffinity="0" condim="6" contype="1" friction="1 0.5 0.0001" group="3" margin="0.001" rgba="0.5 0.6 0.7 .4"
solimp="0.8 0.9 0.001" solref="0.01 1" user="0"/>
<position user="1002 40 2001 -0.0001 0.0001"/>
</default>
</default>
<default class="panda_overlay">
<joint armature="1" damping="1000" frictionloss="10" limited="false"/>
<geom conaffinity="0" contype="0" group="2" rgba=".42 0.42 0.42 .5" type="mesh"/>
</default>
</default>
</mujocoinclude>
fancy_gym/envs/mujoco/box_pushing/assets/robots/controller/panda_torque_control.xml
+11
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<mujoco model="torque">
<actuator>
<motor forcelimited="true" forcerange="-87 87" joint="panda_joint1" name="panda_joint1_act"/>
<motor forcelimited="true" forcerange="-87 87" joint="panda_joint2" name="panda_joint2_act"/>
<motor forcelimited="true" forcerange="-87 87" joint="panda_joint3" name="panda_joint3_act"/>
<motor forcelimited="true" forcerange="-87 87" joint="panda_joint4" name="panda_joint4_act"/>
<motor forcelimited="true" forcerange="-12 12" joint="panda_joint5" name="panda_joint5_act"/>
<motor forcelimited="true" forcerange="-12 12" joint="panda_joint6" name="panda_joint6_act"/>
<motor forcelimited="true" forcerange="-12 12" joint="panda_joint7" name="panda_joint7_act"/>
</actuator>
</mujoco>
fancy_gym/envs/mujoco/box_pushing/assets/robots/controller/panda_velocity_control.xml
+13
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<mujocoinclude>
<actuator>
<velocity ctrlrange="-2.1750 2.1750" forcerange="-87 87" joint="panda_joint1" kv="0.8" name="panda_joint1"/>
<velocity ctrlrange="-2.1750 2.1750" forcerange="-87 87" joint="panda_joint2" kv="0.8" name="panda_joint2"/>
<velocity ctrlrange="-2.1750 2.1750" forcerange="-87 87" joint="panda_joint3" kv="0.8" name="panda_joint3"/>
<velocity ctrlrange="-2.1750 2.1750" forcerange="-87 87" joint="panda_joint4" kv="0.8" name="panda_joint4"/>
<velocity ctrlrange="-2.6100 2.6100" forcerange="-12 12" joint="panda_joint5" kv="0.8" name="panda_joint5"/>
<velocity ctrlrange="-2.6100 2.6100" forcerange="-12 12" joint="panda_joint6" kv="0.8" name="panda_joint6"/>
<velocity ctrlrange="-2.9671 2.9671" forcerange="-12 12" joint="panda_joint7" kv="0.8" name="panda_joint7"/>
</actuator>
</mujocoinclude>
fancy_gym/envs/mujoco/box_pushing/assets/robots/depr/panda_gym.xml
+157
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<mujoco model="panda">
<compiler angle="radian" discardvisual="false" meshdir="../meshes_panda/for_mujoco/"/>
<option cone="elliptic" impratio="20" timestep="0.002">
</option>
<size nconmax="2000" njmax="2000"/>
<asset>
<mesh file="link0v.stl" name="link0v"/>
<mesh file="link1v.stl" name="link1v"/>
<mesh file="link2v.stl" name="link2v"/>
<mesh file="link3v.stl" name="link3v"/>
<mesh file="link4v.stl" name="link4v"/>
<mesh file="link5v.stl" name="link5v"/>
<mesh file="link6v.stl" name="link6v"/>
<mesh file="link7v.stl" name="link7v"/>
<mesh file="handv.stl" name="handv"/>
<mesh file="fingerv.stl" name="fingerv"/>
<texture builtin="gradient" height="32" rgb1="0.26 0.58 0.51" rgb2="0.26 0.58 0.51" type="skybox" width="32"/>
</asset>
<contact>
<exclude body1="panda_link0" body2="panda_link1"/>
<exclude body1="panda_link1" body2="panda_link2"/>
<exclude body1="panda_link2" body2="panda_link3"/>
<exclude body1="panda_link3" body2="panda_link4"/>
<exclude body1="panda_link4" body2="panda_link5"/>
<exclude body1="panda_link5" body2="panda_link6"/>
<exclude body1="panda_link6" body2="panda_link7"/>
<exclude body1="panda_link7" body2="panda_link8"/>
<exclude body1="panda_link8" body2="panda_hand"/>
</contact>
<visual>
<map zfar="1000" znear="0.001"/>
</visual>
<worldbody>
<light castshadow="false" cutoff="100" diffuse="1 1 1" dir="-0 0 -1.3" directional="true" exponent="1" pos="0 0 1.3"
specular=".1 .1 .1"/>
<body name="ground" pos="0 0 -0.94">
<geom name="ground:geom1" rgba="0.26 0.58 0.51 1" size="0 0 1" type="plane"/>
</body>
<body name="panda_link0" pos="0 0 0">
<inertial diaginertia="0.0126801 0.0117603 0.00856656" mass="3.01399" pos="-0.0291898 -0.000879465 0.0566032"
quat="0.00411744 0.564916 0.0132875 0.825031"/>
<geom conaffinity="0" contype="0" group="1" mesh="link0v" name="panda_link0:geom1" rgba="1 1 1 1" type="mesh"/>
<geom mesh="link0v" name="panda_link0:geom2" rgba="1 1 1 1" type="mesh"/>
<body childclass="panda:body" name="panda_link1" pos="0 0 0.333">
<inertial diaginertia="0.0164224 0.0153969 0.00546286" mass="2.77281" pos="1.1399e-05 -0.0312655 -0.0693733"
quat="0.98466 0.174481 -0.000101815 0.000347662"/>
<joint axis="0 0 1" limited="true" name="panda_joint1" pos="0 0 0" range="-2.9671 2.9671"/>
<geom conaffinity="0" contype="0" group="1" mesh="link1v" name="panda_joint1:geom1" rgba="1 1 1 1" type="mesh"/>
<geom mesh="link1v" name="panda_joint1:geom2" rgba="1 1 1 1" type="mesh"/>
<body childclass="panda:body" name="panda_link2" pos="0 0 0" quat="0.707107 -0.707107 0 0">
<inertial diaginertia="0.016787 0.0157415 0.00553027" mass="2.7996" pos="-1.31766e-05 -0.0703216 0.0311782"
quat="0.57484 0.818266 -6.05764e-05 -6.61626e-05"/>
<joint axis="0 0 1" limited="true" name="panda_joint2" pos="0 0 0"
range="-1.8326 1.8326"/>
<geom conaffinity="0" contype="0" group="1" mesh="link2v" name="panda_joint2:geom1" rgba="1 1 1 1" type="mesh"/>
<geom mesh="link2v" name="panda_joint2:geom2" rgba="1 1 1 1" type="mesh"/>
<body childclass="panda:body" name="panda_link3" pos="0 -0.316 0" quat="0.707107 0.707107 0 0">
<inertial diaginertia="0.00915257 0.00896477 0.00384742" mass="2.14603" pos="0.0443483 0.0249283 -0.03813"
quat="0.0615263 0.349824 0.234291 0.904956"/>
<joint axis="0 0 1" limited="true" name="panda_joint3" pos="0 0 0" range="-2.9671 2.9671"/>
<geom conaffinity="0" contype="0" group="1" mesh="link3v" name="panda_joint3:geom1" rgba="1 1 1 1" type="mesh"/>
<geom mesh="link3v" name="panda_joint3:geom2" rgba="1 1 1 1" type="mesh"/>
<body childclass="panda:body" name="panda_link4" pos="0.0825 0 0" quat="0.707107 0.707107 0 0">
<inertial diaginertia="0.00946899 0.00928491 0.00396694" mass="2.18807" pos="-0.0385503 0.0395256 0.0247162"
quat="0.813566 0.465041 0.309792 0.160858"/>
<joint axis="0 0 1" limited="true" name="panda_joint4" pos="0 0 0" range="-3.1416 0.0873"/>
<geom conaffinity="0" contype="0" group="1" mesh="link4v" name="panda_link4:geom1" rgba="1 1 1 1" type="mesh"/>
<geom mesh="link4v" name="panda_link4:geom2" rgba="1 1 1 1" type="mesh"/>
<body childclass="panda:body" name="panda_link5" pos="-0.0825 0.384 0" quat="0.707107 -0.707107 0 0">
<inertial diaginertia="0.0278873 0.0268823 0.00569569" mass="3.19545" pos="-6.36776e-05 0.0384124 -0.10997"
quat="0.990767 -0.135571 0.000963106 0.000694406"/>
<joint axis="0 0 1" limited="true" name="panda_joint5" pos="0 0 0" range="-2.9671 2.9671"/>
<geom conaffinity="0" contype="0" group="1" mesh="link5v" name="panda_link5:geom1" rgba="1 1 1 1" type="mesh"/>
<geom mesh="link5v" name="panda_link5:geom2" rgba="1 1 1 1" type="mesh"/>
<body childclass="panda:body" name="panda_link6" pos="0 0 0" quat="0.707107 0.707107 0 0">
<inertial diaginertia="0.00412168 0.0033698 0.00213304" mass="1.35761" pos="0.0510023 0.00693267 0.00616899"
quat="-0.0460841 0.754362 0.044494 0.653325"/>
<joint axis="0 0 1" limited="true" name="panda_joint6" pos="0 0 0" range="-0.0873 3.8223"/>
<geom conaffinity="0" contype="0" group="1" mesh="link6v" name="panda_link6:geom1" rgba="1 1 1 1" type="mesh"/>
<geom mesh="link6v" name="panda_link6:geom2" rgba="1 1 1 1" type="mesh"/>
<body childclass="panda:body" name="panda_link7" pos="0.088 0 0" quat="0.707107 0.707107 0 0">
<inertial diaginertia="0.000637671 0.000528056 0.000279577" mass="0.417345"
pos="0.0103614 0.0103596 0.0791078" quat="0.63547 0.278021 -0.670462 0.263369"/>
<joint axis="0 0 1" limited="true" name="panda_joint7" pos="0 0 0" range="-2.9671 2.9671"/>
<geom conaffinity="0" contype="0" group="1" mesh="link7v" name="panda_link7:geom1" rgba="1 1 1 1"
type="mesh"/>
<geom mesh="link7v" name="panda_link7:geom2" rgba="1 1 1 1" solimp="1.998 1.999 0" type="mesh"/>
<body name="panda_link8" pos="0 0 0.107">
<inertial diaginertia="0.1 0.1 0.1" mass="0.1" pos="0 0 0"/>
<body name="panda_hand" pos="0 0 0" quat="0.92388 0 0 -0.382683">
<inertial diaginertia="0.00227632 0.00206087 0.000456542" mass="0.670782"
pos="-2.76618e-06 -0.00061547 0.0239295" quat="0.697945 0.716151 -0.000242485 8.47563e-05"/>
<geom conaffinity="0" contype="0" group="1" mesh="handv" name="panda_hand:geom1" type="mesh"/>
<geom mesh="handv" name="panda_hand:geom2" solimp="1.998 1.999 0" type="mesh"/>
<camera euler="0 3.14 0" fovy="60" ipd="0.0" mode="fixed" name="rgbd" pos="0.1 0.0 0."/>
<body name="tcp" pos="0 0 0.1">
<site name="tcp" rgba="0 0 1 0" size="0.001"/>
</body>
<body childclass="panda:gripper" name="panda_leftfinger" pos="0 0 0.0584">
<inertial diaginertia="5.69661e-06 5.56035e-06 1.55183e-06" mass="0.0218754" pos="-2.42335e-05 0.0119585 0.0237816" quat="0.996177 0.0868631 -2.79377e-05 -0.00926642" />
<joint axis="0 1 0" name="panda_finger_joint1" />
<site name="panda_leftfinger:site" pos="0 0.012 0.045" rgba="0 0 1 0" size="0.012 0.015 0.012" type="box" />
<geom conaffinity="0" contype="0" group="1" mesh="fingerv" name="panda_leftfinger:geom1" type="mesh" />
<geom mesh="fingerv" name="panda_leftfinger:geom2" type="mesh" />
<body name="finger_joint1_tip" pos="0 0.0085 0.056">
<inertial diaginertia="0.01 0.01 0.01" mass="0.01" pos="0 0 0" quat="0 0 0 1" />
<geom conaffinity="1" condim="4" contype="1" friction="2 0.05 0.0001" name="finger1_tip_collision" pos="0 -0.005 -0.012" quat="0 0 0 1" rgba="1 0 0 1" size="0.008 0.004 0.008" solref="0.01 0.5" type="box" />
<geom conaffinity="1" contype="1" name="touch_left" rgba="0 1 0 1" pos="0 -0.005 -0.012"
quat="1 1 0 0" size="0.004 0.004" type="cylinder"/>
</body>
</body>
<body childclass="panda:gripper" name="panda_rightfinger" pos="0 0 0.0584">
<inertial diaginertia="5.69661e-06 5.56035e-06 1.55183e-06" mass="0.0218754" pos="2.42335e-05 -0.0119585 0.0237816" quat="0.996177 -0.0868631 2.79377e-05 -0.00926642" />
<joint axis="0 -1 0" name="panda_finger_joint2" />
<site name="panda_rightfinger:site" pos="0 -0.012 0.045" rgba="0 0 1 0" size="0.012 0.015 0.012" type="box" />
<geom conaffinity="0" contype="0" group="1" mesh="fingerv" name="panda_rightfinger:geom1" quat="0 0 0 1" type="mesh" />
<geom mesh="fingerv" name="panda_rightfinger:geom2" quat="0 0 0 1" type="mesh" />
<body name="finger_joint2_tip" pos="0 -0.0085 0.056">
<inertial diaginertia="0.01 0.01 0.01" mass="0.01" pos="0 0 0" quat="0 0 0 1" />
<geom conaffinity="1" condim="4" contype="1" friction="2 0.05 0.0001" name="finger2_tip_collision" pos="0 0.005 -0.012" quat="0 0 0 1" rgba="1 0 0 1" size="0.008 0.004 0.008" solref="0.01 0.5" type="box" />
<geom conaffinity="1" contype="1" name="touch_right" rgba="0 1 0 1" pos="0 0.005 -0.012"
quat="1 1 0 0" size="0.004 0.004" type="cylinder"/>
</body>
</body>
</body>
</body>
</body>
</body>
</body>
</body>
</body>
</body>
</body>
</body>
<camera euler="0 0 0" fovy="45" ipd="0.0" mode="fixed" name="rgbd_cage" pos="0.7 0.1 0.9"/>
</worldbody>
<sensor>
<touch name="touchsensor:left" site="panda_leftfinger:site"/>
<touch name="touchsensor:right" site="panda_rightfinger:site"/>
</sensor>
<default>
<default class="panda:body">
<joint damping="0" />
</default>
<default class="panda:gripper">
<geom condim="4" solimp="0.999 0.999 0.0001" type="box"/>
<joint armature="10" damping="300" limited="true" range="0.001 0.04" type="slide"/>
</default>
</default>
<actuator>
<position ctrllimited="true" ctrlrange="0.001 0.04" forcelimited="true" forcerange="-70 70"
joint="panda_finger_joint1" kp="1000000" name="panda_finger_joint1_act"/>
<position ctrllimited="true" ctrlrange="0.001 0.04" forcelimited="true" forcerange="-70 70"
joint="panda_finger_joint2" kp="1000000" name="panda_finger_joint2_act"/>
</actuator>
<include file="../assets/panda_frame.xml"/>
</mujoco>
fancy_gym/envs/mujoco/box_pushing/assets/robots/panda.xml
+155
View File
@@ -0,0 +1,155 @@
<mujocoinclude>
<compiler angle="radian" discardvisual="false" meshdir="../robots/meshes/panda/"/>
<asset>
<mesh file="link0v.stl" name="link0v"/>
<mesh file="link1v.stl" name="link1v"/>
<mesh file="link2v.stl" name="link2v"/>
<mesh file="link3v.stl" name="link3v"/>
<mesh file="link4v.stl" name="link4v"/>
<mesh file="link5v.stl" name="link5v"/>
<mesh file="link6v.stl" name="link6v"/>
<mesh file="link7v.stl" name="link7v"/>
<mesh file="handv.stl" name="handv"/>
<mesh file="fingerv.stl" name="fingerv"/>
</asset>
<contact>
<exclude body1="panda_link0" body2="panda_link1"/>
<exclude body1="panda_link1" body2="panda_link2"/>
<exclude body1="panda_link2" body2="panda_link3"/>
<exclude body1="panda_link3" body2="panda_link4"/>
<exclude body1="panda_link4" body2="panda_link5"/>
<exclude body1="panda_link5" body2="panda_link6"/>
<exclude body1="panda_link6" body2="panda_link7"/>
<exclude body1="panda_link7" body2="panda_link8"/>
<exclude body1="panda_link8" body2="panda_hand"/>
</contact>
<worldbody>
<body name="panda_link0" pos="0 0 0">
<inertial diaginertia="0.0126801 0.0117603 0.00856656" mass="3.01399" pos="-0.0291898 -0.000879465 0.0566032"
quat="0.00411744 0.564916 0.0132875 0.825031"/>
<geom conaffinity="0" contype="0" group="1" mesh="link0v" name="panda_link0:geom1" rgba="1 1 1 1" type="mesh"/>
<geom mesh="link0v" name="panda_link0:geom2" rgba="1 1 1 1" type="mesh"/>
<body name="panda_link1" pos="0 0 0.333">
<inertial diaginertia="0.0164224 0.0153969 0.00546286" mass="2.77281" pos="1.1399e-05 -0.0312655 -0.0693733"
quat="0.98466 0.174481 -0.000101815 0.000347662"/>
<joint axis="0 0 1" limited="true" name="panda_joint1" pos="0 0 0" range="-2.9671 2.9671"/>
<geom conaffinity="0" contype="0" group="1" mesh="link1v" name="panda_joint1:geom1" rgba="1 1 1 1" type="mesh"/>
<geom mesh="link1v" name="panda_joint1:geom2" rgba="1 1 1 1" type="mesh"/>
<body name="panda_link2" pos="0 0 0" quat="0.707107 -0.707107 0 0">
<inertial diaginertia="0.016787 0.0157415 0.00553027" mass="2.7996" pos="-1.31766e-05 -0.0703216 0.0311782"
quat="0.57484 0.818266 -6.05764e-05 -6.61626e-05"/>
<joint axis="0 0 1" limited="true" name="panda_joint2" pos="0 0 0" range="-1.8326
1.8326"/>
<geom conaffinity="0" contype="0" group="1" mesh="link2v" name="panda_joint2:geom1" rgba="1 1 1 1" type="mesh"/>
<geom mesh="link2v" name="panda_joint2:geom2" rgba="1 1 1 1" type="mesh"/>
<body name="panda_link3" pos="0 -0.316 0" quat="0.707107 0.707107 0 0">
<inertial diaginertia="0.00915257 0.00896477 0.00384742" mass="2.14603" pos="0.0443483 0.0249283 -0.03813"
quat="0.0615263 0.349824 0.234291 0.904956"/>
<joint axis="0 0 1" limited="true" name="panda_joint3" pos="0 0 0" range="-2.9671 2.9671"/>
<geom conaffinity="0" contype="0" group="1" mesh="link3v" name="panda_joint3:geom1" rgba="1 1 1 1" type="mesh"/>
<geom mesh="link3v" name="panda_joint3:geom2" rgba="1 1 1 1" type="mesh"/>
<body name="panda_link4" pos="0.0825 0 0" quat="0.707107 0.707107 0 0">
<inertial diaginertia="0.00946899 0.00928491 0.00396694" mass="2.18807" pos="-0.0385503 0.0395256 0.0247162"
quat="0.813566 0.465041 0.309792 0.160858"/>
<joint axis="0 0 1" limited="true" name="panda_joint4" pos="0 0 0" range="-3.1416 0.0873"/>
<geom conaffinity="0" contype="0" group="1" mesh="link4v" name="panda_link4:geom1" rgba="1 1 1 1" type="mesh"/>
<geom mesh="link4v" name="panda_link4:geom2" rgba="1 1 1 1" type="mesh"/>
<body name="panda_link5" pos="-0.0825 0.384 0" quat="0.707107 -0.707107 0 0">
<inertial diaginertia="0.0278873 0.0268823 0.00569569" mass="3.19545" pos="-6.36776e-05 0.0384124 -0.10997"
quat="0.990767 -0.135571 0.000963106 0.000694406"/>
<joint axis="0 0 1" limited="true" name="panda_joint5" pos="0 0 0" range="-2.9671 2.9671"/>
<geom conaffinity="0" contype="0" group="1" mesh="link5v" name="panda_link5:geom1" rgba="1 1 1 1" type="mesh"/>
<geom mesh="link5v" name="panda_link5:geom2" rgba="1 1 1 1" type="mesh"/>
<body name="panda_link6" pos="0 0 0" quat="0.707107 0.707107 0 0">
<inertial diaginertia="0.00412168 0.0033698 0.00213304" mass="1.35761" pos="0.0510023 0.00693267 0.00616899"
quat="-0.0460841 0.754362 0.044494 0.653325"/>
<joint axis="0 0 1" limited="true" name="panda_joint6" pos="0 0 0" range="-0.0873 3.8223"/>
<geom conaffinity="0" contype="0" group="1" mesh="link6v" name="panda_link6:geom1" rgba="1 1 1 1" type="mesh"/>
<geom mesh="link6v" name="panda_link6:geom2" rgba="1 1 1 1" type="mesh"/>
<body name="panda_link7" pos="0.088 0 0" quat="0.707107 0.707107 0 0">
<inertial diaginertia="0.000637671 0.000528056 0.000279577" mass="0.417345"
pos="0.0103614 0.0103596 0.0791078" quat="0.63547 0.278021 -0.670462 0.263369"/>
<joint axis="0 0 1" limited="true" name="panda_joint7" pos="0 0 0" range="-2.9671 2.9671"/>
<geom conaffinity="0" contype="0" group="1" mesh="link7v" name="panda_link7:geom1" rgba="1 1 1 1"
type="mesh"/>
<geom mesh="link7v" name="panda_link7:geom2" rgba="1 1 1 1" solimp="1.998 1.999 0" type="mesh"/>
<body name="panda_link8" pos="0 0 0.107">
<inertial diaginertia="0.1 0.1 0.1" mass="0.1" pos="0 0 0"/>
<body name="panda_hand" pos="0 0 0" quat="0.92388 0 0 -0.382683">
<inertial diaginertia="0.00227632 0.00206087 0.000456542" mass="0.670782"
pos="-2.76618e-06 -0.00061547 0.0239295" quat="0.697945 0.716151 -0.000242485 8.47563e-05"/>
<geom conaffinity="0" contype="0" group="1" mesh="handv" name="panda_hand:geom1" type="mesh"/>
<geom mesh="handv" name="panda_hand:geom2" solimp="1.998 1.999 0" type="mesh"/>
<camera euler="0 3.14 0" fovy="60" ipd="0.0" mode="fixed" name="rgbd" pos="0.1 0.0 0."/>
<body name="tcp" pos="0 0 0.105">
<site name="tcp" rgba="'0 0 1 0'" size='0.001'/>
</body>
<body childclass="panda:gripper" name="panda_leftfinger" pos="0 0 0.0584">
<inertial diaginertia="5.69661e-06 5.56035e-06 1.55183e-06" mass="0.0218754"
pos="-2.42335e-05 0.0119585 0.0237816" quat="0.996177 0.0868631 -2.79377e-05 -0.00926642"/>
<joint axis="0 1 0" name="panda_finger_joint1"/>
<geom conaffinity="0" contype="0" group="1" mesh="fingerv" name="panda_leftfinger:geom1" type="mesh"/>
<geom mesh="fingerv" name="panda_leftfinger:geom2" type="mesh"/>
<site name="panda_leftfinger:site" pos="0 0.012 0.045" rgba="0 0 1 0" size="0.012 0.015 0.012" type="box"/>
<body name="finger_joint1_tip" pos="0 0.0085 0.056">
<inertial diaginertia="0.01 0.01 0.01" mass="0.01" pos="0 0 0" quat="0 0 0 1"/>
<geom conaffinity="1" condim="4" contype="1" friction="2 0.05 0.0001" name="finger1_tip_collision"
pos="0 -0.005 -0.012" quat="0 0 0 1" rgba="1 0 0 1" size="0.008 0.004 0.008" solref="0.01 0.5"
type="box"/>
</body>
</body>
<body childclass="panda:gripper" name="panda_rightfinger" pos="0 0 0.0584">
<inertial diaginertia="5.69661e-06 5.56035e-06 1.55183e-06" mass="0.0218754"
pos="2.42335e-05 -0.0119585 0.0237816" quat="0.996177 -0.0868631 2.79377e-05 -0.00926642"/>
<joint axis="0 -1 0" name="panda_finger_joint2"/>
<site name="panda_rightfinger:site" pos="0 -0.012 0.045" rgba="0 0 1 0" size="0.012 0.015 0.012"
type="box"/>
<geom conaffinity="0" contype="0" group="1" mesh="fingerv" name="panda_rightfinger:geom1" quat="0 0 0 1"
type="mesh"/>
<geom condim="4" mesh="fingerv" name="panda_rightfinger:geom2" quat="0 0 0 1" type="mesh"/>
<body name="finger_joint2_tip" pos="0 -0.0085 0.056">
<inertial diaginertia="0.01 0.01 0.01" mass="0.01" pos="0 0 0" quat="0 0 0 1"/>
<geom conaffinity="1" condim="4" contype="1" friction="2 0.05 0.0001" name="finger2_tip_collision"
pos="0 0.005 -0.012" quat="0 0 0 1" rgba="1 0 0 1" size="0.008 0.004 0.008" solref="0.01 0.5"
type="box"/>
</body>
</body>
</body>
</body>
</body>
</body>
</body>
</body>
</body>
</body>
</body>
</body>
<camera euler="0 0 0" fovy="45" ipd="0.0" mode="fixed" name="rgbd_cage" pos="0.7 0.1 0.9"/>
</worldbody>
<sensor>
<touch name="touchsensor:left" site="panda_leftfinger:site"/>
<touch name="touchsensor:right" site="panda_rightfinger:site"/>
</sensor>
<default>
<default class="panda:gripper">
<geom condim="4" friction="1 0.005 0.0001" margin="0.001" solimp="0.998 0.999 0.001" solref="0.02 1" type="box"/>
<joint damping="10" limited="true" range="0 0.04" type="slide"/>
</default>
</default>
<actuator>
<motor forcelimited="true" forcerange="-87 87" joint="panda_joint1" name="panda_joint1_act"/>
<motor forcelimited="true" forcerange="-87 87" joint="panda_joint2" name="panda_joint2_act"/>
<motor forcelimited="true" forcerange="-87 87" joint="panda_joint3" name="panda_joint3_act"/>
<motor forcelimited="true" forcerange="-87 87" joint="panda_joint4" name="panda_joint4_act"/>
<motor forcelimited="true" forcerange="-12 12" joint="panda_joint5" name="panda_joint5_act"/>
<motor forcelimited="true" forcerange="-12 12" joint="panda_joint6" name="panda_joint6_act"/>
<motor forcelimited="true" forcerange="-12 12" joint="panda_joint7" name="panda_joint7_act"/>
<motor forcelimited="true" forcerange="-70 70" joint="panda_finger_joint1" name="panda_finger_joint1_act"/>
<motor forcelimited="true" forcerange="-70 70" joint="panda_finger_joint2" name="panda_finger_joint2_act"/>
<!--<position ctrllimited="true" ctrlrange="0.001 0.04" forcelimited="true" forcerange="-70 70"
joint="panda_finger_joint1" kp="1000000" name="panda_finger_joint1_act"/>
<position ctrllimited="true" ctrlrange="0.001 0.04" forcelimited="true" forcerange="-70 70"
joint="panda_finger_joint2" kp="1000000" name="panda_finger_joint2_act"/>-->
</actuator>
</mujocoinclude>
fancy_gym/envs/mujoco/box_pushing/assets/robots/panda_bimanual.xml
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<!--todo-->
fancy_gym/envs/mujoco/box_pushing/assets/robots/panda_mocap.xml
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<mujocoinclude>
<compiler angle="radian" discardvisual="false" meshdir="../robots/meshes/panda/"/>
<asset>
<mesh file="link0v.stl" name="link0v"/>
<mesh file="link1v.stl" name="link1v"/>
<mesh file="link2v.stl" name="link2v"/>
<mesh file="link3v.stl" name="link3v"/>
<mesh file="link4v.stl" name="link4v"/>
<mesh file="link5v.stl" name="link5v"/>
<mesh file="link6v.stl" name="link6v"/>
<mesh file="link7v.stl" name="link7v"/>
<mesh file="handv.stl" name="handv"/>
<mesh file="fingerv.stl" name="fingerv"/>
</asset>
<contact>
<exclude body1="panda_link0" body2="panda_link1"/>
<exclude body1="panda_link1" body2="panda_link2"/>
<exclude body1="panda_link2" body2="panda_link3"/>
<exclude body1="panda_link3" body2="panda_link4"/>
<exclude body1="panda_link4" body2="panda_link5"/>
<exclude body1="panda_link5" body2="panda_link6"/>
<exclude body1="panda_link6" body2="panda_link7"/>
<exclude body1="panda_link7" body2="panda_link8"/>
<exclude body1="panda_link8" body2="panda_hand"/>
</contact>
<visual>
<map zfar="1000" znear="0.001"/>
</visual>
<worldbody>
<body name="panda_link0" pos="0 0 0">
<inertial diaginertia="0.0126801 0.0117603 0.00856656" mass="3.01399" pos="-0.0291898 -0.000879465 0.0566032" quat="0.00411744 0.564916 0.0132875 0.825031"/>
<geom conaffinity="0" contype="0" group="1" mesh="link0v" name="panda_link0:geom1" rgba="1 1 1 1" type="mesh"/>
<geom mesh="link0v" name="panda_link0:geom2" rgba="1 1 1 1" type="mesh"/>
<body name="panda_link1" pos="0 0 0.333">
<inertial diaginertia="0.0164224 0.0153969 0.00546286" mass="2.77281" pos="1.1399e-05 -0.0312655 -0.0693733" quat="0.98466 0.174481 -0.000101815 0.000347662"/>
<joint axis="0 0 1" damping="300" limited="true" name="panda_joint1" pos="0 0 0" range="-2.9671 2.9671"/>
<geom conaffinity="0" contype="0" group="1" mesh="link1v" name="panda_joint1:geom1" rgba="1 1 1 1" type="mesh"/>
<geom mesh="link1v" name="panda_joint1:geom2" rgba="1 1 1 1" type="mesh"/>
<body name="panda_link2" pos="0 0 0" quat="0.707107 -0.707107 0 0">
<inertial diaginertia="0.016787 0.0157415 0.00553027" mass="2.7996" pos="-1.31766e-05 -0.0703216 0.0311782" quat="0.57484 0.818266 -6.05764e-05 -6.61626e-05"/>
<joint axis="0 0 1" damping="300" limited="true" name="panda_joint2" pos="0 0 0" range="-1.8326 1.8326"/>
<geom conaffinity="0" contype="0" group="1" mesh="link2v" name="panda_joint2:geom1" rgba="1 1 1 1" type="mesh"/>
<geom mesh="link2v" name="panda_joint2:geom2" rgba="1 1 1 1" type="mesh"/>
<body name="panda_link3" pos="0 -0.316 0" quat="0.707107 0.707107 0 0">
<inertial diaginertia="0.00915257 0.00896477 0.00384742" mass="2.14603" pos="0.0443483 0.0249283 -0.03813" quat="0.0615263 0.349824 0.234291 0.904956"/>
<joint axis="0 0 1" damping="300" limited="true" name="panda_joint3" pos="0 0 0" range="-2.9671 2.9671"/>
<geom conaffinity="0" contype="0" group="1" mesh="link3v" name="panda_joint3:geom1" rgba="1 1 1 1" type="mesh"/>
<geom mesh="link3v" name="panda_joint3:geom2" rgba="1 1 1 1" type="mesh"/>
<body name="panda_link4" pos="0.0825 0 0" quat="0.707107 0.707107 0 0">
<inertial diaginertia="0.00946899 0.00928491 0.00396694" mass="2.18807" pos="-0.0385503 0.0395256 0.0247162" quat="0.813566 0.465041 0.309792 0.160858"/>
<joint axis="0 0 1" damping="300" limited="true" name="panda_joint4" pos="0 0 0" range="-3.1416 0.0873"/>
<geom conaffinity="0" contype="0" group="1" mesh="link4v" name="panda_link4:geom1" rgba="1 1 1 1" type="mesh"/>
<geom mesh="link4v" name="panda_link4:geom2" rgba="1 1 1 1" type="mesh"/>
<body name="panda_link5" pos="-0.0825 0.384 0" quat="0.707107 -0.707107 0 0">
<inertial diaginertia="0.0278873 0.0268823 0.00569569" mass="3.19545" pos="-6.36776e-05 0.0384124 -0.10997" quat="0.990767 -0.135571 0.000963106 0.000694406"/>
<joint axis="0 0 1" damping="300" limited="true" name="panda_joint5" pos="0 0 0" range="-2.9671 2.9671"/>
<geom conaffinity="0" contype="0" group="1" mesh="link5v" name="panda_link5:geom1" rgba="1 1 1 1" type="mesh"/>
<geom mesh="link5v" name="panda_link5:geom2" rgba="1 1 1 1" type="mesh"/>
<body name="panda_link6" pos="0 0 0" quat="0.707107 0.707107 0 0">
<inertial diaginertia="0.00412168 0.0033698 0.00213304" mass="1.35761" pos="0.0510023 0.00693267 0.00616899" quat="-0.0460841 0.754362 0.044494 0.653325"/>
<joint axis="0 0 1" damping="300" limited="true" name="panda_joint6" pos="0 0 0" range="-0.0873 3.8223"/>
<geom conaffinity="0" contype="0" group="1" mesh="link6v" name="panda_link6:geom1" rgba="1 1 1 1" type="mesh"/>
<geom mesh="link6v" name="panda_link6:geom2" rgba="1 1 1 1" type="mesh"/>
<body name="panda_link7" pos="0.088 0 0" quat="0.707107 0.707107 0 0">
<inertial diaginertia="0.000637671 0.000528056 0.000279577" mass="0.417345" pos="0.0103614 0.0103596 0.0791078" quat="0.63547 0.278021 -0.670462 0.263369"/>
<joint axis="0 0 1" damping="300" limited="true" name="panda_joint7" pos="0 0 0" range="-2.9671 2.9671"/>
<geom conaffinity="0" contype="0" group="1" mesh="link7v" name="panda_link7:geom1" rgba="1 1 1 1" type="mesh"/>
<geom mesh="link7v" name="panda_link7:geom2" rgba="1 1 1 1" solimp="1.998 1.999 0" type="mesh"/>
<body name="panda_link8" pos="0 0 0.107">
<inertial diaginertia="0.1 0.1 0.1" mass="0.1" pos="0 0 0"/>
<body name="panda_hand" pos="0 0 0" quat="0.92388 0 0 -0.382683">
<inertial diaginertia="0.00227632 0.00206087 0.000456542" mass="0.670782" pos="-2.76618e-06 -0.00061547 0.0239295" quat="0.697945 0.716151 -0.000242485 8.47563e-05"/>
<geom conaffinity="0" contype="0" group="1" mesh="handv" name="panda_hand:geom1" type="mesh"/>
<geom mesh="handv" name="panda_hand:geom2" solimp="1.998 1.999 0" type="mesh"/>
<camera euler="0 3.14 0" fovy="60" ipd="0.0" mode="fixed" name="rgbd" pos="0.1 0.0 0."/>
<body name="tcp" pos="0 0 0.105">
<site name="tcp" rgba="'0 0 1 0'" size='0.001'/>
</body>
<body childclass="panda:gripper" name="panda_leftfinger" pos="0 0 0.0584">
<inertial diaginertia="5.69661e-06 5.56035e-06 1.55183e-06" mass="0.0218754" pos="-2.42335e-05 0.0119585 0.0237816" quat="0.996177 0.0868631 -2.79377e-05 -0.00926642"/>
<!-- <joint axis="0 1 0" limited="true" name="panda_finger_joint1" pos="0 0 0" range="0 0.04"-->
<!-- type="slide" />-->
<joint axis="0 1 0" name="panda_finger_joint1"/>
<site name="panda_leftfinger:site" pos="0 0.012 0.045" rgba="0 0 1 0" size="0.012 0.015 0.012" type="box"/>
<geom conaffinity="0" contype="0" group="1" mesh="fingerv" name="panda_leftfinger:geom1" type="mesh"/>
<geom mesh="fingerv" name="panda_leftfinger:geom2" type="mesh"/>
<body name="finger_joint1_tip" pos="0 0.0085 0.056">
<inertial diaginertia="0.01 0.01 0.01" mass="0.01" pos="0 0 0" quat="0 0 0 1"/>
<geom conaffinity="1" condim="4" contype="1" friction="2 0.05 0.0001" name="finger1_tip_collision" pos="0 -0.005 -0.012" quat="0 0 0 1" rgba="1 0 0 1" size="0.008 0.004 0.008" solref="0.01 0.5" type="box"/>
</body>
</body>
<body childclass="panda:gripper" name="panda_rightfinger" pos="0 0 0.0584">
<inertial diaginertia="5.69661e-06 5.56035e-06 1.55183e-06" mass="0.0218754" pos="2.42335e-05 -0.0119585 0.0237816" quat="0.996177 -0.0868631 2.79377e-05 -0.00926642"/>
<joint axis="0 -1 0" name="panda_finger_joint2"/>
<site name="panda_rightfinger:site" pos="0 -0.012 0.045" rgba="0 0 1 0" size="0.012 0.015 0.012" type="box"/>
<geom conaffinity="0" contype="0" group="1" mesh="fingerv" name="panda_rightfinger:geom1" quat="0 0 0 1" type="mesh"/>
<geom condim="4" mesh="fingerv" name="panda_rightfinger:geom2" quat="0 0 0 1" type="mesh"/>
<body name="finger_joint2_tip" pos="0 -0.0085 0.056">
<inertial diaginertia="0.01 0.01 0.01" mass="0.01" pos="0 0 0" quat="0 0 0 1"/>
<geom conaffinity="1" condim="4" contype="1" friction="2 0.05 0.0001" name="finger2_tip_collision" pos="0 0.005 -0.012" quat="0 0 0 1" rgba="1 0 0 1" size="0.008 0.004 0.008" solref="0.01 0.5" type="box"/>
</body>
</body>
</body>
</body>
</body>
</body>
</body>
</body>
</body>
</body>
</body>
</body>
<camera euler="0 0 0" fovy="45" ipd="0.0" mode="fixed" name="rgbd_cage" pos="0.7 0.1 0.9"/>
<body mocap="true" name="panda:mocap" pos="0 0 0">
<geom conaffinity="0" contype="0" pos="0 0 0" rgba="0 0.5 0 0.7" size="0.005 0.005 0.005" type="box"/>
<!-- <geom conaffinity="0" contype="0" pos="0 0 0" rgba="1 0 0 0.3" size="1 0.005 0.005" type="box" />-->
<!-- <geom conaffinity="0" contype="0" pos="0 0 0" rgba="0 1 0 0.3" size="0.005 1 0.005" type="box" />-->
<!-- <geom conaffinity="0" contype="0" pos="0 0 0" rgba="0 0 1 0.3" size="0.005 0.005 1" type="box" />-->
</body>
</worldbody>
<equality>
<weld body1="panda:mocap" body2="tcp" solref="0.001 1"/>
</equality>
<sensor>
<touch name="touchsensor:left" site="panda_leftfinger:site"/>
<touch name="touchsensor:right" site="panda_rightfinger:site"/>
</sensor>
<default>
<default class="panda:gripper">
<geom condim="4" friction="1 0.005 0.0001" margin="0.001" solimp="0.998 0.999 0.001" solref="0.02 1" type="box"/>
<joint damping="10" limited="true" range="0 0.04" type="slide"/>
</default>
</default>
<actuator>
<position ctrllimited="true" ctrlrange="0.001 0.04" forcelimited="true" forcerange="-70 70"
joint="panda_finger_joint1" kp="1000000" name="panda_finger_joint1_act"/>
<position ctrllimited="true" ctrlrange="0.001 0.04" forcelimited="true" forcerange="-70 70"
joint="panda_finger_joint2" kp="1000000" name="panda_finger_joint2_act"/>
</actuator>
</mujocoinclude>
fancy_gym/envs/mujoco/box_pushing/assets/robots/pandas_kit_lab.xml
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<!--todo-->
fancy_gym/envs/mujoco/box_pushing/assets/surroundings/base.xml
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<mujoco model="base_surrounding">
<compiler angle="radian" discardvisual="false"/>
<option collision="all" cone="elliptic" gravity="0 0 -9.81" impratio="3" solver="Newton" timestep="0.001"
tolerance="1e-10"/>
<size nconmax="2000" njmax="2000"/>
<asset>
<texture builtin="gradient" height="32" rgb1="0.26 0.58 0.51" rgb2="0.26 0.58 0.51" type="skybox" width="32"/>
</asset>
<visual>
<map zfar="1000" znear="0.001"/>
</visual>
<worldbody>
<light castshadow="true" cutoff="100" diffuse="1 1 1" dir="-0 0 -1.3" directional="true" exponent="1" pos="0 0 1.3"
specular=".1 .1 .1"/>
<body name="ground" pos="0 0 -0.94">
<geom name="ground:geom1" rgba="0.26 0.58 0.51 1" size="0 0 1" type="plane"/>
</body>
</worldbody>
</mujoco>
fancy_gym/envs/mujoco/box_pushing/box_pushing_env.py
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import os
import numpy as np
from gym import utils, spaces
from gym.envs.mujoco import MujocoEnv
from fancy_gym.envs.mujoco.box_pushing.box_pushing_utils import rot_to_quat, get_quaternion_error, rotation_distance
from fancy_gym.envs.mujoco.box_pushing.box_pushing_utils import q_max, q_min, q_dot_max, q_torque_max
from fancy_gym.envs.mujoco.box_pushing.box_pushing_utils import desired_rod_quat
import mujoco
MAX_EPISODE_STEPS_BOX_PUSHING = 100
BOX_POS_BOUND = np.array([[0.3, -0.45, -0.01], [0.6, 0.45, -0.01]])
class BoxPushingEnvBase(MujocoEnv, utils.EzPickle):
"""
franka box pushing environment
action space:
normalized joints torque * 7 , range [-1, 1]
observation space:
rewards:
1. dense reward
2. time-depend sparse reward
3. time-spatial-depend sparse reward
"""
def __init__(self, frame_skip: int = 10):
utils.EzPickle.__init__(**locals())
self._steps = 0
self.init_qpos_box_pushing = np.array([0., 0., 0., -1.5, 0., 1.5, 0., 0., 0., 0.6, 0.45, 0.0, 1., 0., 0., 0.])
self.init_qvel_box_pushing = np.zeros(15)
self.frame_skip = frame_skip
self._q_max = q_max
self._q_min = q_min
self._q_dot_max = q_dot_max
self._desired_rod_quat = desired_rod_quat
self._episode_energy = 0.
MujocoEnv.__init__(self,
model_path=os.path.join(os.path.dirname(__file__), "assets", "box_pushing.xml"),
frame_skip=self.frame_skip,
mujoco_bindings="mujoco")
self.action_space = spaces.Box(low=-1, high=1, shape=(7,))
def step(self, action):
action = 10 * np.clip(action, self.action_space.low, self.action_space.high)
resultant_action = np.clip(action + self.data.qfrc_bias[:7].copy(), -q_torque_max, q_torque_max)
unstable_simulation = False
try:
self.do_simulation(resultant_action, self.frame_skip)
except Exception as e:
print(e)
unstable_simulation = True
self._steps += 1
self._episode_energy += np.sum(np.square(action))
episode_end = True if self._steps >= MAX_EPISODE_STEPS_BOX_PUSHING else False
box_pos = self.data.body("box_0").xpos.copy()
box_quat = self.data.body("box_0").xquat.copy()
target_pos = self.data.body("replan_target_pos").xpos.copy()
target_quat = self.data.body("replan_target_pos").xquat.copy()
rod_tip_pos = self.data.site("rod_tip").xpos.copy()
rod_quat = self.data.body("push_rod").xquat.copy()
qpos = self.data.qpos[:7].copy()
qvel = self.data.qvel[:7].copy()
if not unstable_simulation:
reward = self._get_reward(episode_end, box_pos, box_quat, target_pos, target_quat,
rod_tip_pos, rod_quat, qpos, qvel, action)
else:
reward = -50
obs = self._get_obs()
box_goal_pos_dist = 0. if not episode_end else np.linalg.norm(box_pos - target_pos)
box_goal_quat_dist = 0. if not episode_end else rotation_distance(box_quat, target_quat)
infos = {
'episode_end': episode_end,
'box_goal_pos_dist': box_goal_pos_dist,
'box_goal_rot_dist': box_goal_quat_dist,
'episode_energy': 0. if not episode_end else self._episode_energy,
'is_success': True if episode_end and box_goal_pos_dist < 0.05 and box_goal_quat_dist < 0.5 else False,
'num_steps': self._steps
}
return obs, reward, episode_end, infos
def reset_model(self):
# rest box to initial position
self.set_state(self.init_qpos_box_pushing, self.init_qvel_box_pushing)
box_init_pos = np.array([0.4, 0.3, -0.01, 0.0, 0.0, 0.0, 1.0])
self.data.joint("box_joint").qpos = box_init_pos
# set target position
box_target_pos = self.sample_context()
while np.linalg.norm(box_target_pos[:2] - box_init_pos[:2]) < 0.3:
box_target_pos = self.sample_context()
# box_target_pos[0] = 0.4
# box_target_pos[1] = -0.3
# box_target_pos[-4:] = np.array([0.0, 0.0, 0.0, 1.0])
self.model.body_pos[2] = box_target_pos[:3]
self.model.body_quat[2] = box_target_pos[-4:]
self.model.body_pos[3] = box_target_pos[:3]
self.model.body_quat[3] = box_target_pos[-4:]
# set the robot to the right configuration (rod tip in the box)
desired_tcp_pos = box_init_pos[:3] + np.array([0.0, 0.0, 0.15])
desired_tcp_quat = np.array([0, 1, 0, 0])
desired_joint_pos = self.calculateOfflineIK(desired_tcp_pos, desired_tcp_quat)
self.data.qpos[:7] = desired_joint_pos
mujoco.mj_forward(self.model, self.data)
self._steps = 0
self._episode_energy = 0.
return self._get_obs()
def sample_context(self):
pos = self.np_random.uniform(low=BOX_POS_BOUND[0], high=BOX_POS_BOUND[1])
theta = self.np_random.uniform(low=0, high=np.pi * 2)
quat = rot_to_quat(theta, np.array([0, 0, 1]))
return np.concatenate([pos, quat])
def _get_reward(self, episode_end, box_pos, box_quat, target_pos, target_quat,
rod_tip_pos, rod_quat, qpos, qvel, action):
raise NotImplementedError
def _get_obs(self):
obs = np.concatenate([
self.data.qpos[:7].copy(), # joint position
self.data.qvel[:7].copy(), # joint velocity
# self.data.qfrc_bias[:7].copy(), # joint gravity compensation
# self.data.site("rod_tip").xpos.copy(), # position of rod tip
# self.data.body("push_rod").xquat.copy(), # orientation of rod
self.data.body("box_0").xpos.copy(), # position of box
self.data.body("box_0").xquat.copy(), # orientation of box
self.data.body("replan_target_pos").xpos.copy(), # position of target
self.data.body("replan_target_pos").xquat.copy() # orientation of target
])
return obs
def _joint_limit_violate_penalty(self, qpos, qvel, enable_pos_limit=False, enable_vel_limit=False):
penalty = 0.
p_coeff = 1.
v_coeff = 1.
# q_limit
if enable_pos_limit:
higher_error = qpos - self._q_max
lower_error = self._q_min - qpos
penalty -= p_coeff * (abs(np.sum(higher_error[qpos > self._q_max])) +
abs(np.sum(lower_error[qpos < self._q_min])))
# q_dot_limit
if enable_vel_limit:
q_dot_error = abs(qvel) - abs(self._q_dot_max)
penalty -= v_coeff * abs(np.sum(q_dot_error[q_dot_error > 0.]))
return penalty
def get_body_jacp(self, name):
id = mujoco.mj_name2id(self.model, 1, name)
jacp = np.zeros((3, self.model.nv))
mujoco.mj_jacBody(self.model, self.data, jacp, None, id)
return jacp
def get_body_jacr(self, name):
id = mujoco.mj_name2id(self.model, 1, name)
jacr = np.zeros((3, self.model.nv))
mujoco.mj_jacBody(self.model, self.data, None, jacr, id)
return jacr
def calculateOfflineIK(self, desired_cart_pos, desired_cart_quat):
"""
calculate offline inverse kinematics for franka pandas
:param desired_cart_pos: desired cartesian position of tool center point
:param desired_cart_quat: desired cartesian quaternion of tool center point
:return: joint angles
"""
J_reg = 1e-6
w = np.diag([1, 1, 1, 1, 1, 1, 1])
target_theta_null = np.array([
3.57795216e-09,
1.74532920e-01,
3.30500960e-08,
-8.72664630e-01,
-1.14096181e-07,
1.22173047e00,
7.85398126e-01])
eps = 1e-5 # threshold for convergence
IT_MAX = 1000
dt = 1e-3
i = 0
pgain = [
33.9403713446798,
30.9403713446798,
33.9403713446798,
27.69370238555632,
33.98706171459314,
30.9185531893281,
]
pgain_null = 5 * np.array([
7.675519770796831,
2.676935478437176,
8.539040163444975,
1.270446361314313,
8.87752182480855,
2.186782233762969,
4.414432577659688,
])
pgain_limit = 20
q = self.data.qpos[:7].copy()
qd_d = np.zeros(q.shape)
old_err_norm = np.inf
while True:
q_old = q
q = q + dt * qd_d
q = np.clip(q, q_min, q_max)
self.data.qpos[:7] = q
mujoco.mj_forward(self.model, self.data)
current_cart_pos = self.data.body("tcp").xpos.copy()
current_cart_quat = self.data.body("tcp").xquat.copy()
cart_pos_error = np.clip(desired_cart_pos - current_cart_pos, -0.1, 0.1)
if np.linalg.norm(current_cart_quat - desired_cart_quat) > np.linalg.norm(current_cart_quat + desired_cart_quat):
current_cart_quat = -current_cart_quat
cart_quat_error = np.clip(get_quaternion_error(current_cart_quat, desired_cart_quat), -0.5, 0.5)
err = np.hstack((cart_pos_error, cart_quat_error))
err_norm = np.sum(cart_pos_error**2) + np.sum((current_cart_quat - desired_cart_quat)**2)
if err_norm > old_err_norm:
q = q_old
dt = 0.7 * dt
continue
else:
dt = 1.025 * dt
if err_norm < eps:
break
if i > IT_MAX:
break
old_err_norm = err_norm
### get Jacobian by mujoco
self.data.qpos[:7] = q
mujoco.mj_forward(self.model, self.data)
jacp = self.get_body_jacp("tcp")[:, :7].copy()
jacr = self.get_body_jacr("tcp")[:, :7].copy()
J = np.concatenate((jacp, jacr), axis=0)
Jw = J.dot(w)
# J * W * J.T + J_reg * I
JwJ_reg = Jw.dot(J.T) + J_reg * np.eye(J.shape[0])
# Null space velocity, points to home position
qd_null = pgain_null * (target_theta_null - q)
margin_to_limit = 0.1
qd_null_limit = np.zeros(qd_null.shape)
qd_null_limit_max = pgain_limit * (q_max - margin_to_limit - q)
qd_null_limit_min = pgain_limit * (q_min + margin_to_limit - q)
qd_null_limit[q > q_max - margin_to_limit] += qd_null_limit_max[q > q_max - margin_to_limit]
qd_null_limit[q < q_min + margin_to_limit] += qd_null_limit_min[q < q_min + margin_to_limit]
qd_null += qd_null_limit
# W J.T (J W J' + reg I)^-1 xd_d + (I - W J.T (J W J' + reg I)^-1 J qd_null
qd_d = np.linalg.solve(JwJ_reg, pgain * err - J.dot(qd_null))
qd_d = w.dot(J.transpose()).dot(qd_d) + qd_null
i += 1
return q
class BoxPushingDense(BoxPushingEnvBase):
def __init__(self, frame_skip: int = 10):
super(BoxPushingDense, self).__init__(frame_skip=frame_skip)
def _get_reward(self, episode_end, box_pos, box_quat, target_pos, target_quat,
rod_tip_pos, rod_quat, qpos, qvel, action):
joint_penalty = self._joint_limit_violate_penalty(qpos,
qvel,
enable_pos_limit=True,
enable_vel_limit=True)
tcp_box_dist_reward = -2 * np.clip(np.linalg.norm(box_pos - rod_tip_pos), 0.05, 100)
box_goal_pos_dist_reward = -3.5 * np.linalg.norm(box_pos - target_pos)
box_goal_rot_dist_reward = -rotation_distance(box_quat, target_quat) / np.pi
energy_cost = -0.0005 * np.sum(np.square(action))
reward = joint_penalty + tcp_box_dist_reward + \
box_goal_pos_dist_reward + box_goal_rot_dist_reward + energy_cost
rod_inclined_angle = rotation_distance(rod_quat, self._desired_rod_quat)
if rod_inclined_angle > np.pi / 4:
reward -= rod_inclined_angle / (np.pi)
return reward
class BoxPushingTemporalSparse(BoxPushingEnvBase):
def __init__(self, frame_skip: int = 10):
super(BoxPushingTemporalSparse, self).__init__(frame_skip=frame_skip)
def _get_reward(self, episode_end, box_pos, box_quat, target_pos, target_quat,
rod_tip_pos, rod_quat, qpos, qvel, action):
reward = 0.
joint_penalty = self._joint_limit_violate_penalty(qpos, qvel, enable_pos_limit=True, enable_vel_limit=True)
energy_cost = -0.0005 * np.sum(np.square(action))
tcp_box_dist_reward = -2 * np.clip(np.linalg.norm(box_pos - rod_tip_pos), 0.05, 100)
reward += joint_penalty + tcp_box_dist_reward + energy_cost
rod_inclined_angle = rotation_distance(rod_quat, desired_rod_quat)
if rod_inclined_angle > np.pi / 4:
reward -= rod_inclined_angle / (np.pi)
if not episode_end:
return reward
box_goal_dist = np.linalg.norm(box_pos - target_pos)
box_goal_pos_dist_reward = -3.5 * box_goal_dist * 100
box_goal_rot_dist_reward = -rotation_distance(box_quat, target_quat) / np.pi * 100
reward += box_goal_pos_dist_reward + box_goal_rot_dist_reward
return reward
class BoxPushingTemporalSpatialSparse(BoxPushingEnvBase):
def __init__(self, frame_skip: int = 10):
super(BoxPushingTemporalSpatialSparse, self).__init__(frame_skip=frame_skip)
def _get_reward(self, episode_end, box_pos, box_quat, target_pos, target_quat,
rod_tip_pos, rod_quat, qpos, qvel, action):
reward = 0.
joint_penalty = self._joint_limit_violate_penalty(qpos, qvel, enable_pos_limit=True, enable_vel_limit=True)
energy_cost = -0.0005 * np.sum(np.square(action))
tcp_box_dist_reward = -2 * np.clip(np.linalg.norm(box_pos - rod_tip_pos), 0.05, 100)
reward += joint_penalty + tcp_box_dist_reward + energy_cost
rod_inclined_angle = rotation_distance(rod_quat, desired_rod_quat)
if rod_inclined_angle > np.pi / 4:
reward -= rod_inclined_angle / (np.pi)
if not episode_end:
return reward
box_goal_dist = np.linalg.norm(box_pos - target_pos)
if box_goal_dist < 0.1:
reward += 300
box_goal_pos_dist_reward = np.clip(- 3.5 * box_goal_dist * 100 * 3, -100, 0)
box_goal_rot_dist_reward = np.clip(- rotation_distance(box_quat, target_quat)/np.pi * 100 * 1.5, -100, 0)
reward += box_goal_pos_dist_reward + box_goal_rot_dist_reward
return reward
fancy_gym/envs/mujoco/box_pushing/box_pushing_utils.py
+53
View File
@@ -0,0 +1,53 @@
import numpy as np
# joint constraints for Franka robot
q_max = np.array([2.8973, 1.7628, 2.8973, -0.0698, 2.8973, 3.7525, 2.8973])
q_min = np.array([-2.8973, -1.7628, -2.8973, -3.0718, -2.8973, -0.0175, -2.8973])
q_dot_max = np.array([2.1750, 2.1750, 2.1750, 2.1750, 2.6100, 2.6100, 2.6100])
q_torque_max = np.array([90., 90., 90., 90., 12., 12., 12.])
#
desired_rod_quat = np.array([0.0, 1.0, 0.0, 0.0])
def skew(x):
"""
Returns the skew-symmetric matrix of x
param x: 3x1 vector
"""
return np.array([[0, -x[2], x[1]], [x[2], 0, -x[0]], [-x[1], x[0], 0]])
def get_quaternion_error(curr_quat, des_quat):
"""
Calculates the difference between the current quaternion and the desired quaternion.
See Siciliano textbook page 140 Eq 3.91
param curr_quat: current quaternion
param des_quat: desired quaternion
return: difference between current quaternion and desired quaternion
"""
return curr_quat[0] * des_quat[1:] - des_quat[0] * curr_quat[1:] - skew(des_quat[1:]) @ curr_quat[1:]
def rotation_distance(p: np.array, q: np.array):
"""
Calculates the rotation angular between two quaternions
param p: quaternion
param q: quaternion
theta: rotation angle between p and q (rad)
"""
assert p.shape == q.shape, "p and q should be quaternion"
theta = 2 * np.arccos(abs(p @ q))
return theta
def rot_to_quat(theta, axis):
"""
Converts rotation angle along an axis to quaternion
param theta: rotation angle (rad)
param axis: rotation axis
return: quaternion
"""
quant = np.zeros(4)
quant[0] = np.sin(theta / 2.)
quant[1:] = np.cos(theta / 2.) * axis
return quant
fancy_gym/envs/mujoco/box_pushing/mp_wrapper.py
+29
View File
@@ -0,0 +1,29 @@
from typing import Union, Tuple
import numpy as np
from fancy_gym.black_box.raw_interface_wrapper import RawInterfaceWrapper
class MPWrapper(RawInterfaceWrapper):
# Random x goal + random init pos
@property
def context_mask(self):
return np.hstack([
[False] * 7, # joints position
[False] * 7, # joints velocity
[False] * 3, # position of box
[False] * 4, # orientation of box
[True] * 3, # position of target
[True] * 4, # orientation of target
# [True] * 1, # time
])
@property
def current_pos(self) -> Union[float, int, np.ndarray, Tuple]:
return self.data.qpos[:7].copy()
@property
def current_vel(self) -> Union[float, int, np.ndarray, Tuple]:
return self.data.qvel[:7].copy()
fancy_gym/examples/example_replanning_envs.py
+62
View File
@@ -0,0 +1,62 @@
import fancy_gym
def example_run_replanning_env(env_name="BoxPushingDenseReplanProDMP-v0", seed=1, iterations=1, render=False):
env = fancy_gym.make(env_name, seed=seed)
env.reset()
for i in range(iterations):
done = False
while done is False:
ac = env.action_space.sample()
obs, reward, done, info = env.step(ac)
if render:
env.render(mode="human")
if done:
env.reset()
env.close()
del env
def example_custom_replanning_envs(seed=0, iteration=100, render=True):
# id for a step-based environment
base_env_id = "BoxPushingDense-v0"
wrappers = [fancy_gym.envs.mujoco.box_pushing.mp_wrapper.MPWrapper]
trajectory_generator_kwargs = {'trajectory_generator_type': 'prodmp',
'weight_scale': 1}
phase_generator_kwargs = {'phase_generator_type': 'exp'}
controller_kwargs = {'controller_type': 'velocity'}
basis_generator_kwargs = {'basis_generator_type': 'prodmp',
'num_basis': 5}
# max_planning_times: the maximum number of plans can be generated
# replanning_schedule: the trigger for replanning
# condition_on_desired: use desired state as the boundary condition for the next plan
black_box_kwargs = {'max_planning_times': 4,
'replanning_schedule': lambda pos, vel, obs, action, t: t % 25 == 0,
'condition_on_desired': True}
env = fancy_gym.make_bb(env_id=base_env_id, wrappers=wrappers, black_box_kwargs=black_box_kwargs,
traj_gen_kwargs=trajectory_generator_kwargs, controller_kwargs=controller_kwargs,
phase_kwargs=phase_generator_kwargs, basis_kwargs=basis_generator_kwargs,
seed=seed)
if render:
env.render(mode="human")
obs = env.reset()
for i in range(iteration):
ac = env.action_space.sample()
obs, reward, done, info = env.step(ac)
if done:
env.reset()
env.close()
del env
if __name__ == "__main__":
# run a registered replanning environment
example_run_replanning_env(env_name="BoxPushingDenseReplanProDMP-v0", seed=1, iterations=1, render=False)
# run a custom replanning environment
example_custom_replanning_envs(seed=0, iteration=8, render=True)
fancy_gym/examples/examples_movement_primitives.py
+6 -2
View File
@@ -24,7 +24,7 @@ def example_mp(env_name="HoleReacherProMP-v0", seed=1, iterations=1, render=True
# number of samples/full trajectories (multiple environment steps) # number of samples/full trajectories (multiple environment steps)
for i in range(iterations): for i in range(iterations):
if render and i % 2 == 0: if render and i % 1 == 0:
# This renders the full MP trajectory # This renders the full MP trajectory
# It is only required to call render() once in the beginning, which renders every consecutive trajectory. # It is only required to call render() once in the beginning, which renders every consecutive trajectory.
# Resetting to no rendering, can be achieved by render(mode=None). # Resetting to no rendering, can be achieved by render(mode=None).
@@ -155,12 +155,16 @@ def example_fully_custom_mp(seed=1, iterations=1, render=True):
if __name__ == '__main__': if __name__ == '__main__':
render = False render = True
# DMP # DMP
example_mp("HoleReacherDMP-v0", seed=10, iterations=5, render=render) example_mp("HoleReacherDMP-v0", seed=10, iterations=5, render=render)
# ProMP # ProMP
example_mp("HoleReacherProMP-v0", seed=10, iterations=5, render=render) example_mp("HoleReacherProMP-v0", seed=10, iterations=5, render=render)
example_mp("BoxPushingTemporalSparseProMP-v0", seed=10, iterations=1, render=render)
# ProDMP
example_mp("BoxPushingDenseReplanProDMP-v0", seed=10, iterations=4, render=render)
# Altered basis functions # Altered basis functions
obs1 = example_custom_mp("Reacher5dProMP-v0", seed=10, iterations=1, render=render) obs1 = example_custom_mp("Reacher5dProMP-v0", seed=10, iterations=1, render=render)
fancy_gym/meta/README.MD
+1 -1
View File
@@ -1,6 +1,6 @@
# MetaWorld Wrappers # MetaWorld Wrappers
These are the Environment Wrappers for selected [Metaworld](https://meta-world.github.io/) environments in order to use our Motion Primitive gym interface with them. These are the Environment Wrappers for selected [Metaworld](https://meta-world.github.io/) environments in order to use our Movement Primitive gym interface with them.
All Metaworld environments have a 39 dimensional observation space with the same structure. The tasks differ only in the objective and the initial observations that are randomized. All Metaworld environments have a 39 dimensional observation space with the same structure. The tasks differ only in the objective and the initial observations that are randomized.
Unused observations are zeroed out. E.g. for `Button-Press-v2` the observation mask looks the following: Unused observations are zeroed out. E.g. for `Button-Press-v2` the observation mask looks the following:
```python ```python
fancy_gym/meta/__init__.py
+83 -1
View File
@@ -5,7 +5,7 @@ from gym import register
from . import goal_object_change_mp_wrapper, goal_change_mp_wrapper, goal_endeffector_change_mp_wrapper, \ from . import goal_object_change_mp_wrapper, goal_change_mp_wrapper, goal_endeffector_change_mp_wrapper, \
object_change_mp_wrapper object_change_mp_wrapper
ALL_METAWORLD_MOVEMENT_PRIMITIVE_ENVIRONMENTS = {"DMP": [], "ProMP": []} ALL_METAWORLD_MOVEMENT_PRIMITIVE_ENVIRONMENTS = {"DMP": [], "ProMP": [], "ProDMP": []}
# MetaWorld # MetaWorld
@@ -28,11 +28,35 @@ DEFAULT_BB_DICT_ProMP = {
} }
} }
DEFAULT_BB_DICT_ProDMP = {
"name": 'EnvName',
"wrappers": [],
"trajectory_generator_kwargs": {
'trajectory_generator_type': 'prodmp'
},
"phase_generator_kwargs": {
'phase_generator_type': 'exp'
},
"controller_kwargs": {
'controller_type': 'metaworld',
},
"basis_generator_kwargs": {
'basis_generator_type': 'prodmp',
'num_basis': 5
},
"black_box_kwargs": {
'replanning_schedule': None,
'max_planning_times': None,
}
}
_goal_change_envs = ["assembly-v2", "pick-out-of-hole-v2", "plate-slide-v2", "plate-slide-back-v2", _goal_change_envs = ["assembly-v2", "pick-out-of-hole-v2", "plate-slide-v2", "plate-slide-back-v2",
"plate-slide-side-v2", "plate-slide-back-side-v2"] "plate-slide-side-v2", "plate-slide-back-side-v2"]
for _task in _goal_change_envs: for _task in _goal_change_envs:
task_id_split = _task.split("-") task_id_split = _task.split("-")
name = "".join([s.capitalize() for s in task_id_split[:-1]]) name = "".join([s.capitalize() for s in task_id_split[:-1]])
# ProMP
_env_id = f'{name}ProMP-{task_id_split[-1]}' _env_id = f'{name}ProMP-{task_id_split[-1]}'
kwargs_dict_goal_change_promp = deepcopy(DEFAULT_BB_DICT_ProMP) kwargs_dict_goal_change_promp = deepcopy(DEFAULT_BB_DICT_ProMP)
kwargs_dict_goal_change_promp['wrappers'].append(goal_change_mp_wrapper.MPWrapper) kwargs_dict_goal_change_promp['wrappers'].append(goal_change_mp_wrapper.MPWrapper)
@@ -45,10 +69,25 @@ for _task in _goal_change_envs:
) )
ALL_METAWORLD_MOVEMENT_PRIMITIVE_ENVIRONMENTS["ProMP"].append(_env_id) ALL_METAWORLD_MOVEMENT_PRIMITIVE_ENVIRONMENTS["ProMP"].append(_env_id)
# ProDMP
_env_id = f'{name}ProDMP-{task_id_split[-1]}'
kwargs_dict_goal_change_prodmp = deepcopy(DEFAULT_BB_DICT_ProDMP)
kwargs_dict_goal_change_prodmp['wrappers'].append(goal_change_mp_wrapper.MPWrapper)
kwargs_dict_goal_change_prodmp['name'] = f'metaworld:{_task}'
register(
id=_env_id,
entry_point='fancy_gym.utils.make_env_helpers:make_bb_env_helper',
kwargs=kwargs_dict_goal_change_prodmp
)
ALL_METAWORLD_MOVEMENT_PRIMITIVE_ENVIRONMENTS["ProDMP"].append(_env_id)
_object_change_envs = ["bin-picking-v2", "hammer-v2", "sweep-into-v2"] _object_change_envs = ["bin-picking-v2", "hammer-v2", "sweep-into-v2"]
for _task in _object_change_envs: for _task in _object_change_envs:
task_id_split = _task.split("-") task_id_split = _task.split("-")
name = "".join([s.capitalize() for s in task_id_split[:-1]]) name = "".join([s.capitalize() for s in task_id_split[:-1]])
# ProMP
_env_id = f'{name}ProMP-{task_id_split[-1]}' _env_id = f'{name}ProMP-{task_id_split[-1]}'
kwargs_dict_object_change_promp = deepcopy(DEFAULT_BB_DICT_ProMP) kwargs_dict_object_change_promp = deepcopy(DEFAULT_BB_DICT_ProMP)
kwargs_dict_object_change_promp['wrappers'].append(object_change_mp_wrapper.MPWrapper) kwargs_dict_object_change_promp['wrappers'].append(object_change_mp_wrapper.MPWrapper)
@@ -60,6 +99,18 @@ for _task in _object_change_envs:
) )
ALL_METAWORLD_MOVEMENT_PRIMITIVE_ENVIRONMENTS["ProMP"].append(_env_id) ALL_METAWORLD_MOVEMENT_PRIMITIVE_ENVIRONMENTS["ProMP"].append(_env_id)
# ProDMP
_env_id = f'{name}ProDMP-{task_id_split[-1]}'
kwargs_dict_object_change_prodmp = deepcopy(DEFAULT_BB_DICT_ProDMP)
kwargs_dict_object_change_prodmp['wrappers'].append(object_change_mp_wrapper.MPWrapper)
kwargs_dict_object_change_prodmp['name'] = f'metaworld:{_task}'
register(
id=_env_id,
entry_point='fancy_gym.utils.make_env_helpers:make_bb_env_helper',
kwargs=kwargs_dict_object_change_prodmp
)
ALL_METAWORLD_MOVEMENT_PRIMITIVE_ENVIRONMENTS["ProDMP"].append(_env_id)
_goal_and_object_change_envs = ["box-close-v2", "button-press-v2", "button-press-wall-v2", "button-press-topdown-v2", _goal_and_object_change_envs = ["box-close-v2", "button-press-v2", "button-press-wall-v2", "button-press-topdown-v2",
"button-press-topdown-wall-v2", "coffee-button-v2", "coffee-pull-v2", "button-press-topdown-wall-v2", "coffee-button-v2", "coffee-pull-v2",
"coffee-push-v2", "dial-turn-v2", "disassemble-v2", "door-close-v2", "coffee-push-v2", "dial-turn-v2", "disassemble-v2", "door-close-v2",
@@ -74,6 +125,8 @@ _goal_and_object_change_envs = ["box-close-v2", "button-press-v2", "button-press
for _task in _goal_and_object_change_envs: for _task in _goal_and_object_change_envs:
task_id_split = _task.split("-") task_id_split = _task.split("-")
name = "".join([s.capitalize() for s in task_id_split[:-1]]) name = "".join([s.capitalize() for s in task_id_split[:-1]])
# ProMP
_env_id = f'{name}ProMP-{task_id_split[-1]}' _env_id = f'{name}ProMP-{task_id_split[-1]}'
kwargs_dict_goal_and_object_change_promp = deepcopy(DEFAULT_BB_DICT_ProMP) kwargs_dict_goal_and_object_change_promp = deepcopy(DEFAULT_BB_DICT_ProMP)
kwargs_dict_goal_and_object_change_promp['wrappers'].append(goal_object_change_mp_wrapper.MPWrapper) kwargs_dict_goal_and_object_change_promp['wrappers'].append(goal_object_change_mp_wrapper.MPWrapper)
@@ -86,10 +139,26 @@ for _task in _goal_and_object_change_envs:
) )
ALL_METAWORLD_MOVEMENT_PRIMITIVE_ENVIRONMENTS["ProMP"].append(_env_id) ALL_METAWORLD_MOVEMENT_PRIMITIVE_ENVIRONMENTS["ProMP"].append(_env_id)
# ProDMP
_env_id = f'{name}ProDMP-{task_id_split[-1]}'
kwargs_dict_goal_and_object_change_prodmp = deepcopy(DEFAULT_BB_DICT_ProDMP)
kwargs_dict_goal_and_object_change_prodmp['wrappers'].append(goal_object_change_mp_wrapper.MPWrapper)
kwargs_dict_goal_and_object_change_prodmp['name'] = f'metaworld:{_task}'
register(
id=_env_id,
entry_point='fancy_gym.utils.make_env_helpers:make_bb_env_helper',
kwargs=kwargs_dict_goal_and_object_change_prodmp
)
ALL_METAWORLD_MOVEMENT_PRIMITIVE_ENVIRONMENTS["ProDMP"].append(_env_id)
_goal_and_endeffector_change_envs = ["basketball-v2"] _goal_and_endeffector_change_envs = ["basketball-v2"]
for _task in _goal_and_endeffector_change_envs: for _task in _goal_and_endeffector_change_envs:
task_id_split = _task.split("-") task_id_split = _task.split("-")
name = "".join([s.capitalize() for s in task_id_split[:-1]]) name = "".join([s.capitalize() for s in task_id_split[:-1]])
# ProMP
_env_id = f'{name}ProMP-{task_id_split[-1]}' _env_id = f'{name}ProMP-{task_id_split[-1]}'
kwargs_dict_goal_and_endeffector_change_promp = deepcopy(DEFAULT_BB_DICT_ProMP) kwargs_dict_goal_and_endeffector_change_promp = deepcopy(DEFAULT_BB_DICT_ProMP)
kwargs_dict_goal_and_endeffector_change_promp['wrappers'].append(goal_endeffector_change_mp_wrapper.MPWrapper) kwargs_dict_goal_and_endeffector_change_promp['wrappers'].append(goal_endeffector_change_mp_wrapper.MPWrapper)
@@ -101,3 +170,16 @@ for _task in _goal_and_endeffector_change_envs:
kwargs=kwargs_dict_goal_and_endeffector_change_promp kwargs=kwargs_dict_goal_and_endeffector_change_promp
) )
ALL_METAWORLD_MOVEMENT_PRIMITIVE_ENVIRONMENTS["ProMP"].append(_env_id) ALL_METAWORLD_MOVEMENT_PRIMITIVE_ENVIRONMENTS["ProMP"].append(_env_id)
# ProDMP
_env_id = f'{name}ProDMP-{task_id_split[-1]}'
kwargs_dict_goal_and_endeffector_change_prodmp = deepcopy(DEFAULT_BB_DICT_ProDMP)
kwargs_dict_goal_and_endeffector_change_prodmp['wrappers'].append(goal_endeffector_change_mp_wrapper.MPWrapper)
kwargs_dict_goal_and_endeffector_change_prodmp['name'] = f'metaworld:{_task}'
register(
id=_env_id,
entry_point='fancy_gym.utils.make_env_helpers:make_bb_env_helper',
kwargs=kwargs_dict_goal_and_endeffector_change_prodmp
)
ALL_METAWORLD_MOVEMENT_PRIMITIVE_ENVIRONMENTS["ProDMP"].append(_env_id)
fancy_gym/open_ai/__init__.py
+1 -1
View File
@@ -5,7 +5,7 @@ from gym import register
from . import mujoco from . import mujoco
from .deprecated_needs_gym_robotics import robotics from .deprecated_needs_gym_robotics import robotics
ALL_GYM_MOVEMENT_PRIMITIVE_ENVIRONMENTS = {"DMP": [], "ProMP": []} ALL_GYM_MOVEMENT_PRIMITIVE_ENVIRONMENTS = {"DMP": [], "ProMP": [], "ProDMP": []}
DEFAULT_BB_DICT_ProMP = { DEFAULT_BB_DICT_ProMP = {
"name": 'EnvName', "name": 'EnvName',
setup.py
+12 -7
View File
@@ -20,12 +20,17 @@ setup(
name='fancy_gym', name='fancy_gym',
version='0.2', version='0.2',
classifiers=[ classifiers=[
# Python 3.7 is minimally supported 'Development Status :: 3 - Alpha',
"Programming Language :: Python :: 3", 'Intended Audience :: Science/Research',
"Programming Language :: Python :: 3.7", 'License :: OSI Approved :: MIT License',
"Programming Language :: Python :: 3.8", 'Natural Language :: English',
"Programming Language :: Python :: 3.9", 'Operating System :: OS Independent',
"Programming Language :: Python :: 3.10", 'Topic :: Scientific/Engineering :: Artificial Intelligence',
'Programming Language :: Python :: 3',
'Programming Language :: Python :: 3.7',
'Programming Language :: Python :: 3.8',
'Programming Language :: Python :: 3.9',
'Programming Language :: Python :: 3.10',
], ],
extras_require=extras, extras_require=extras,
install_requires=[ install_requires=[
@@ -40,7 +45,7 @@ setup(
}, },
python_requires=">=3.7", python_requires=">=3.7",
url='https://github.com/ALRhub/fancy_gym/', url='https://github.com/ALRhub/fancy_gym/',
# license='AGPL-3.0 license', license='MIT license',
author_email='', author_email='',
description='Fancy Gym: Unifying interface for various RL benchmarks with support for Black Box approaches.' description='Fancy Gym: Unifying interface for various RL benchmarks with support for Black Box approaches.'
) )
test/test_black_box.py
+43 -26
View File
@@ -67,28 +67,32 @@ def test_missing_wrapper(env_id: str):
fancy_gym.make_bb(env_id, [], {}, {}, {}, {}, {}) fancy_gym.make_bb(env_id, [], {}, {}, {}, {}, {})
@pytest.mark.parametrize('mp_type', ['promp', 'dmp']) @pytest.mark.parametrize('mp_type', ['promp', 'dmp', 'prodmp'])
def test_missing_local_state(mp_type: str): def test_missing_local_state(mp_type: str):
basis_generator_type = 'prodmp' if mp_type == 'prodmp' else 'rbf'
env = fancy_gym.make_bb('toy-v0', [RawInterfaceWrapper], {}, env = fancy_gym.make_bb('toy-v0', [RawInterfaceWrapper], {},
{'trajectory_generator_type': mp_type}, {'trajectory_generator_type': mp_type},
{'controller_type': 'motor'}, {'controller_type': 'motor'},
{'phase_generator_type': 'exp'}, {'phase_generator_type': 'exp'},
{'basis_generator_type': 'rbf'}) {'basis_generator_type': basis_generator_type})
env.reset() env.reset()
with pytest.raises(NotImplementedError): with pytest.raises(NotImplementedError):
env.step(env.action_space.sample()) env.step(env.action_space.sample())
@pytest.mark.parametrize('mp_type', ['promp', 'dmp']) @pytest.mark.parametrize('mp_type', ['promp', 'dmp', 'prodmp'])
@pytest.mark.parametrize('env_wrap', zip(ENV_IDS, WRAPPERS)) @pytest.mark.parametrize('env_wrap', zip(ENV_IDS, WRAPPERS))
@pytest.mark.parametrize('verbose', [1, 2]) @pytest.mark.parametrize('verbose', [1, 2])
def test_verbosity(mp_type: str, env_wrap: Tuple[str, Type[RawInterfaceWrapper]], verbose: int): def test_verbosity(mp_type: str, env_wrap: Tuple[str, Type[RawInterfaceWrapper]], verbose: int):
basis_generator_type = 'prodmp' if mp_type == 'prodmp' else 'rbf'
env_id, wrapper_class = env_wrap env_id, wrapper_class = env_wrap
env = fancy_gym.make_bb(env_id, [wrapper_class], {'verbose': verbose}, env = fancy_gym.make_bb(env_id, [wrapper_class], {'verbose': verbose},
{'trajectory_generator_type': mp_type}, {'trajectory_generator_type': mp_type},
{'controller_type': 'motor'}, {'controller_type': 'motor'},
{'phase_generator_type': 'exp'}, {'phase_generator_type': 'exp'},
{'basis_generator_type': 'rbf'}) {'basis_generator_type': basis_generator_type})
env.reset() env.reset()
info_keys = list(env.step(env.action_space.sample())[3].keys()) info_keys = list(env.step(env.action_space.sample())[3].keys())
@@ -104,15 +108,17 @@ def test_verbosity(mp_type: str, env_wrap: Tuple[str, Type[RawInterfaceWrapper]]
assert all(e in info_keys for e in mp_keys) assert all(e in info_keys for e in mp_keys)
@pytest.mark.parametrize('mp_type', ['promp', 'dmp']) @pytest.mark.parametrize('mp_type', ['promp', 'dmp', 'prodmp'])
@pytest.mark.parametrize('env_wrap', zip(ENV_IDS, WRAPPERS)) @pytest.mark.parametrize('env_wrap', zip(ENV_IDS, WRAPPERS))
def test_length(mp_type: str, env_wrap: Tuple[str, Type[RawInterfaceWrapper]]): def test_length(mp_type: str, env_wrap: Tuple[str, Type[RawInterfaceWrapper]]):
basis_generator_type = 'prodmp' if mp_type == 'prodmp' else 'rbf'
env_id, wrapper_class = env_wrap env_id, wrapper_class = env_wrap
env = fancy_gym.make_bb(env_id, [wrapper_class], {}, env = fancy_gym.make_bb(env_id, [wrapper_class], {},
{'trajectory_generator_type': mp_type}, {'trajectory_generator_type': mp_type},
{'controller_type': 'motor'}, {'controller_type': 'motor'},
{'phase_generator_type': 'exp'}, {'phase_generator_type': 'exp'},
{'basis_generator_type': 'rbf'}) {'basis_generator_type': basis_generator_type})
for _ in range(5): for _ in range(5):
env.reset() env.reset()
@@ -121,14 +127,15 @@ def test_length(mp_type: str, env_wrap: Tuple[str, Type[RawInterfaceWrapper]]):
assert length == env.spec.max_episode_steps assert length == env.spec.max_episode_steps
@pytest.mark.parametrize('mp_type', ['promp', 'dmp']) @pytest.mark.parametrize('mp_type', ['promp', 'dmp', 'prodmp'])
@pytest.mark.parametrize('reward_aggregation', [np.sum, np.mean, np.median, lambda x: np.mean(x[::2])]) @pytest.mark.parametrize('reward_aggregation', [np.sum, np.mean, np.median, lambda x: np.mean(x[::2])])
def test_aggregation(mp_type: str, reward_aggregation: Callable[[np.ndarray], float]): def test_aggregation(mp_type: str, reward_aggregation: Callable[[np.ndarray], float]):
basis_generator_type = 'prodmp' if mp_type == 'prodmp' else 'rbf'
env = fancy_gym.make_bb('toy-v0', [ToyWrapper], {'reward_aggregation': reward_aggregation}, env = fancy_gym.make_bb('toy-v0', [ToyWrapper], {'reward_aggregation': reward_aggregation},
{'trajectory_generator_type': mp_type}, {'trajectory_generator_type': mp_type},
{'controller_type': 'motor'}, {'controller_type': 'motor'},
{'phase_generator_type': 'exp'}, {'phase_generator_type': 'exp'},
{'basis_generator_type': 'rbf'}) {'basis_generator_type': basis_generator_type})
env.reset() env.reset()
# ToyEnv only returns 1 as reward # ToyEnv only returns 1 as reward
assert env.step(env.action_space.sample())[1] == reward_aggregation(np.ones(50, )) assert env.step(env.action_space.sample())[1] == reward_aggregation(np.ones(50, ))
@@ -149,12 +156,13 @@ def test_context_space(mp_type: str, env_wrap: Tuple[str, Type[RawInterfaceWrapp
assert env.observation_space.shape == wrapper.context_mask[wrapper.context_mask].shape assert env.observation_space.shape == wrapper.context_mask[wrapper.context_mask].shape
@pytest.mark.parametrize('mp_type', ['promp', 'dmp']) @pytest.mark.parametrize('mp_type', ['promp', 'dmp', 'prodmp'])
@pytest.mark.parametrize('num_dof', [0, 1, 2, 5]) @pytest.mark.parametrize('num_dof', [0, 1, 2, 5])
@pytest.mark.parametrize('num_basis', [0, 1, 2, 5]) @pytest.mark.parametrize('num_basis', [0, 1, 2, 5])
@pytest.mark.parametrize('learn_tau', [True, False]) @pytest.mark.parametrize('learn_tau', [True, False])
@pytest.mark.parametrize('learn_delay', [True, False]) @pytest.mark.parametrize('learn_delay', [True, False])
def test_action_space(mp_type: str, num_dof: int, num_basis: int, learn_tau: bool, learn_delay: bool): def test_action_space(mp_type: str, num_dof: int, num_basis: int, learn_tau: bool, learn_delay: bool):
basis_generator_type = 'prodmp' if mp_type == 'prodmp' else 'rbf'
env = fancy_gym.make_bb('toy-v0', [ToyWrapper], {}, env = fancy_gym.make_bb('toy-v0', [ToyWrapper], {},
{'trajectory_generator_type': mp_type, {'trajectory_generator_type': mp_type,
'action_dim': num_dof 'action_dim': num_dof
@@ -164,28 +172,29 @@ def test_action_space(mp_type: str, num_dof: int, num_basis: int, learn_tau: boo
'learn_tau': learn_tau, 'learn_tau': learn_tau,
'learn_delay': learn_delay 'learn_delay': learn_delay
}, },
{'basis_generator_type': 'rbf', {'basis_generator_type': basis_generator_type,
'num_basis': num_basis 'num_basis': num_basis
}) })
base_dims = num_dof * num_basis base_dims = num_dof * num_basis
additional_dims = num_dof if mp_type == 'dmp' else 0 additional_dims = num_dof if 'dmp' in mp_type else 0
traj_modification_dims = int(learn_tau) + int(learn_delay) traj_modification_dims = int(learn_tau) + int(learn_delay)
assert env.action_space.shape[0] == base_dims + traj_modification_dims + additional_dims assert env.action_space.shape[0] == base_dims + traj_modification_dims + additional_dims
@pytest.mark.parametrize('mp_type', ['promp', 'dmp']) @pytest.mark.parametrize('mp_type', ['promp', 'dmp', 'prodmp'])
@pytest.mark.parametrize('a', [1]) @pytest.mark.parametrize('a', [1])
@pytest.mark.parametrize('b', [1.0]) @pytest.mark.parametrize('b', [1.0])
@pytest.mark.parametrize('c', [[1], [1.0], ['str'], [{'a': 'b'}], [np.ones(3, )]]) @pytest.mark.parametrize('c', [[1], [1.0], ['str'], [{'a': 'b'}], [np.ones(3, )]])
@pytest.mark.parametrize('d', [{'a': 1}, {1: 2.0}, {'a': [1.0]}, {'a': np.ones(3, )}, {'a': {'a': 'b'}}]) @pytest.mark.parametrize('d', [{'a': 1}, {1: 2.0}, {'a': [1.0]}, {'a': np.ones(3, )}, {'a': {'a': 'b'}}])
@pytest.mark.parametrize('e', [Object()]) @pytest.mark.parametrize('e', [Object()])
def test_change_env_kwargs(mp_type: str, a: int, b: float, c: list, d: dict, e: Object): def test_change_env_kwargs(mp_type: str, a: int, b: float, c: list, d: dict, e: Object):
basis_generator_type = 'prodmp' if mp_type == 'prodmp' else 'rbf'
env = fancy_gym.make_bb('toy-v0', [ToyWrapper], {}, env = fancy_gym.make_bb('toy-v0', [ToyWrapper], {},
{'trajectory_generator_type': mp_type}, {'trajectory_generator_type': mp_type},
{'controller_type': 'motor'}, {'controller_type': 'motor'},
{'phase_generator_type': 'exp'}, {'phase_generator_type': 'exp'},
{'basis_generator_type': 'rbf'}, {'basis_generator_type': basis_generator_type},
a=a, b=b, c=c, d=d, e=e a=a, b=b, c=c, d=d, e=e
) )
assert a is env.a assert a is env.a
@@ -196,18 +205,20 @@ def test_change_env_kwargs(mp_type: str, a: int, b: float, c: list, d: dict, e:
assert e is env.e assert e is env.e
@pytest.mark.parametrize('mp_type', ['promp']) @pytest.mark.parametrize('mp_type', ['promp', 'prodmp'])
@pytest.mark.parametrize('tau', [0.25, 0.5, 0.75, 1]) @pytest.mark.parametrize('tau', [0.25, 0.5, 0.75, 1])
def test_learn_tau(mp_type: str, tau: float): def test_learn_tau(mp_type: str, tau: float):
phase_generator_type = 'exp' if mp_type == 'prodmp' else 'linear'
basis_generator_type = 'prodmp' if mp_type == 'prodmp' else 'rbf'
env = fancy_gym.make_bb('toy-v0', [ToyWrapper], {'verbose': 2}, env = fancy_gym.make_bb('toy-v0', [ToyWrapper], {'verbose': 2},
{'trajectory_generator_type': mp_type, {'trajectory_generator_type': mp_type,
}, },
{'controller_type': 'motor'}, {'controller_type': 'motor'},
{'phase_generator_type': 'linear', {'phase_generator_type': phase_generator_type,
'learn_tau': True, 'learn_tau': True,
'learn_delay': False 'learn_delay': False
}, },
{'basis_generator_type': 'rbf', {'basis_generator_type': basis_generator_type,
}, seed=SEED) }, seed=SEED)
d = True d = True
@@ -228,26 +239,29 @@ def test_learn_tau(mp_type: str, tau: float):
vel = info['velocities'].flatten() vel = info['velocities'].flatten()
# Check end is all same (only true for linear basis) # Check end is all same (only true for linear basis)
if phase_generator_type == "linear":
assert np.all(pos[tau_time_steps:] == pos[-1]) assert np.all(pos[tau_time_steps:] == pos[-1])
assert np.all(vel[tau_time_steps:] == vel[-1]) assert np.all(vel[tau_time_steps:] == vel[-1])
# Check active trajectory section is different to end values # Check active trajectory section is different to end values
assert np.all(pos[:tau_time_steps - 1] != pos[-1]) assert np.all(pos[:tau_time_steps - 1] != pos[-1])
assert np.all(vel[:tau_time_steps - 2] != vel[-1]) assert np.all(vel[:tau_time_steps - 2] != vel[-1])
#
#
@pytest.mark.parametrize('mp_type', ['promp']) @pytest.mark.parametrize('mp_type', ['promp', 'prodmp'])
@pytest.mark.parametrize('delay', [0, 0.25, 0.5, 0.75]) @pytest.mark.parametrize('delay', [0, 0.25, 0.5, 0.75])
def test_learn_delay(mp_type: str, delay: float): def test_learn_delay(mp_type: str, delay: float):
basis_generator_type = 'prodmp' if mp_type == 'prodmp' else 'rbf'
phase_generator_type = 'exp' if mp_type == 'prodmp' else 'linear'
env = fancy_gym.make_bb('toy-v0', [ToyWrapper], {'verbose': 2}, env = fancy_gym.make_bb('toy-v0', [ToyWrapper], {'verbose': 2},
{'trajectory_generator_type': mp_type, {'trajectory_generator_type': mp_type,
}, },
{'controller_type': 'motor'}, {'controller_type': 'motor'},
{'phase_generator_type': 'linear', {'phase_generator_type': phase_generator_type,
'learn_tau': False, 'learn_tau': False,
'learn_delay': True 'learn_delay': True
}, },
{'basis_generator_type': 'rbf', {'basis_generator_type': basis_generator_type,
}, seed=SEED) }, seed=SEED)
d = True d = True
@@ -274,21 +288,23 @@ def test_learn_delay(mp_type: str, delay: float):
# Check active trajectory section is different to beginning values # Check active trajectory section is different to beginning values
assert np.all(pos[max(1, delay_time_steps):] != pos[0]) assert np.all(pos[max(1, delay_time_steps):] != pos[0])
assert np.all(vel[max(1, delay_time_steps)] != vel[0]) assert np.all(vel[max(1, delay_time_steps)] != vel[0])
#
#
@pytest.mark.parametrize('mp_type', ['promp']) @pytest.mark.parametrize('mp_type', ['promp', 'prodmp'])
@pytest.mark.parametrize('tau', [0.25, 0.5, 0.75, 1]) @pytest.mark.parametrize('tau', [0.25, 0.5, 0.75, 1])
@pytest.mark.parametrize('delay', [0.25, 0.5, 0.75, 1]) @pytest.mark.parametrize('delay', [0.25, 0.5, 0.75, 1])
def test_learn_tau_and_delay(mp_type: str, tau: float, delay: float): def test_learn_tau_and_delay(mp_type: str, tau: float, delay: float):
phase_generator_type = 'exp' if mp_type == 'prodmp' else 'linear'
basis_generator_type = 'prodmp' if mp_type == 'prodmp' else 'rbf'
env = fancy_gym.make_bb('toy-v0', [ToyWrapper], {'verbose': 2}, env = fancy_gym.make_bb('toy-v0', [ToyWrapper], {'verbose': 2},
{'trajectory_generator_type': mp_type, {'trajectory_generator_type': mp_type,
}, },
{'controller_type': 'motor'}, {'controller_type': 'motor'},
{'phase_generator_type': 'linear', {'phase_generator_type': phase_generator_type,
'learn_tau': True, 'learn_tau': True,
'learn_delay': True 'learn_delay': True
}, },
{'basis_generator_type': 'rbf', {'basis_generator_type': basis_generator_type,
}, seed=SEED) }, seed=SEED)
if env.spec.max_episode_steps * env.dt < delay + tau: if env.spec.max_episode_steps * env.dt < delay + tau:
@@ -315,6 +331,7 @@ def test_learn_tau_and_delay(mp_type: str, tau: float, delay: float):
vel = info['velocities'].flatten() vel = info['velocities'].flatten()
# Check end is all same (only true for linear basis) # Check end is all same (only true for linear basis)
if phase_generator_type == "linear":
assert np.all(pos[joint_time_steps:] == pos[-1]) assert np.all(pos[joint_time_steps:] == pos[-1])
assert np.all(vel[joint_time_steps:] == vel[-1]) assert np.all(vel[joint_time_steps:] == vel[-1])
test/test_replanning_sequencing.py
+192 -3
View File
@@ -98,7 +98,7 @@ def test_learn_sub_trajectories(mp_type: str, env_wrap: Tuple[str, Type[RawInter
assert length <= np.round(env.traj_gen.tau.numpy() / env.dt) assert length <= np.round(env.traj_gen.tau.numpy() / env.dt)
@pytest.mark.parametrize('mp_type', ['promp', 'dmp']) @pytest.mark.parametrize('mp_type', ['promp', 'dmp', 'prodmp'])
@pytest.mark.parametrize('env_wrap', zip(ENV_IDS, WRAPPERS)) @pytest.mark.parametrize('env_wrap', zip(ENV_IDS, WRAPPERS))
@pytest.mark.parametrize('add_time_aware_wrapper_before', [True, False]) @pytest.mark.parametrize('add_time_aware_wrapper_before', [True, False])
@pytest.mark.parametrize('replanning_time', [10, 100, 1000]) @pytest.mark.parametrize('replanning_time', [10, 100, 1000])
@@ -114,11 +114,14 @@ def test_replanning_time(mp_type: str, env_wrap: Tuple[str, Type[RawInterfaceWra
replanning_schedule = lambda c_pos, c_vel, obs, c_action, t: t % replanning_time == 0 replanning_schedule = lambda c_pos, c_vel, obs, c_action, t: t % replanning_time == 0
basis_generator_type = 'prodmp' if mp_type == 'prodmp' else 'rbf'
phase_generator_type = 'exp' if 'dmp' in mp_type else 'linear'
env = fancy_gym.make_bb(env_id, [wrapper_class], {'replanning_schedule': replanning_schedule, 'verbose': 2}, env = fancy_gym.make_bb(env_id, [wrapper_class], {'replanning_schedule': replanning_schedule, 'verbose': 2},
{'trajectory_generator_type': mp_type}, {'trajectory_generator_type': mp_type},
{'controller_type': 'motor'}, {'controller_type': 'motor'},
{'phase_generator_type': 'exp'}, {'phase_generator_type': phase_generator_type},
{'basis_generator_type': 'rbf'}, seed=SEED) {'basis_generator_type': basis_generator_type}, seed=SEED)
assert env.do_replanning assert env.do_replanning
assert callable(env.replanning_schedule) assert callable(env.replanning_schedule)
@@ -142,3 +145,189 @@ def test_replanning_time(mp_type: str, env_wrap: Tuple[str, Type[RawInterfaceWra
env.reset() env.reset()
assert replanning_schedule(None, None, None, None, length) assert replanning_schedule(None, None, None, None, length)
@pytest.mark.parametrize('mp_type', ['promp', 'prodmp'])
@pytest.mark.parametrize('max_planning_times', [1, 2, 3, 4])
@pytest.mark.parametrize('sub_segment_steps', [5, 10])
def test_max_planning_times(mp_type: str, max_planning_times: int, sub_segment_steps: int):
basis_generator_type = 'prodmp' if mp_type == 'prodmp' else 'rbf'
phase_generator_type = 'exp' if mp_type == 'prodmp' else 'linear'
env = fancy_gym.make_bb('toy-v0', [ToyWrapper],
{'max_planning_times': max_planning_times,
'replanning_schedule': lambda pos, vel, obs, action, t: t % sub_segment_steps == 0,
'verbose': 2},
{'trajectory_generator_type': mp_type,
},
{'controller_type': 'motor'},
{'phase_generator_type': phase_generator_type,
'learn_tau': False,
'learn_delay': False
},
{'basis_generator_type': basis_generator_type,
},
seed=SEED)
_ = env.reset()
d = False
planning_times = 0
while not d:
_, _, d, _ = env.step(env.action_space.sample())
planning_times += 1
assert planning_times == max_planning_times
@pytest.mark.parametrize('mp_type', ['promp', 'prodmp'])
@pytest.mark.parametrize('max_planning_times', [1, 2, 3, 4])
@pytest.mark.parametrize('sub_segment_steps', [5, 10])
@pytest.mark.parametrize('tau', [0.5, 1.0, 1.5, 2.0])
def test_replanning_with_learn_tau(mp_type: str, max_planning_times: int, sub_segment_steps: int, tau: float):
basis_generator_type = 'prodmp' if mp_type == 'prodmp' else 'rbf'
phase_generator_type = 'exp' if mp_type == 'prodmp' else 'linear'
env = fancy_gym.make_bb('toy-v0', [ToyWrapper],
{'replanning_schedule': lambda pos, vel, obs, action, t: t % sub_segment_steps == 0,
'max_planning_times': max_planning_times,
'verbose': 2},
{'trajectory_generator_type': mp_type,
},
{'controller_type': 'motor'},
{'phase_generator_type': phase_generator_type,
'learn_tau': True,
'learn_delay': False
},
{'basis_generator_type': basis_generator_type,
},
seed=SEED)
_ = env.reset()
d = False
planning_times = 0
while not d:
action = env.action_space.sample()
action[0] = tau
_, _, d, info = env.step(action)
planning_times += 1
assert planning_times == max_planning_times
@pytest.mark.parametrize('mp_type', ['promp', 'prodmp'])
@pytest.mark.parametrize('max_planning_times', [1, 2, 3, 4])
@pytest.mark.parametrize('sub_segment_steps', [5, 10])
@pytest.mark.parametrize('delay', [0.1, 0.25, 0.5, 0.75])
def test_replanning_with_learn_delay(mp_type: str, max_planning_times: int, sub_segment_steps: int, delay: float):
basis_generator_type = 'prodmp' if mp_type == 'prodmp' else 'rbf'
phase_generator_type = 'exp' if mp_type == 'prodmp' else 'linear'
env = fancy_gym.make_bb('toy-v0', [ToyWrapper],
{'replanning_schedule': lambda pos, vel, obs, action, t: t % sub_segment_steps == 0,
'max_planning_times': max_planning_times,
'verbose': 2},
{'trajectory_generator_type': mp_type,
},
{'controller_type': 'motor'},
{'phase_generator_type': phase_generator_type,
'learn_tau': False,
'learn_delay': True
},
{'basis_generator_type': basis_generator_type,
},
seed=SEED)
_ = env.reset()
d = False
planning_times = 0
while not d:
action = env.action_space.sample()
action[0] = delay
_, _, d, info = env.step(action)
delay_time_steps = int(np.round(delay / env.dt))
pos = info['positions'].flatten()
vel = info['velocities'].flatten()
# Check beginning is all same (only true for linear basis)
if planning_times == 0:
assert np.all(pos[:max(1, delay_time_steps - 1)] == pos[0])
assert np.all(vel[:max(1, delay_time_steps - 2)] == vel[0])
# only valid when delay < sub_segment_steps
elif planning_times > 0 and delay_time_steps < sub_segment_steps:
assert np.all(pos[1:max(1, delay_time_steps - 1)] != pos[0])
assert np.all(vel[1:max(1, delay_time_steps - 2)] != vel[0])
# Check active trajectory section is different to beginning values
assert np.all(pos[max(1, delay_time_steps):] != pos[0])
assert np.all(vel[max(1, delay_time_steps)] != vel[0])
planning_times += 1
assert planning_times == max_planning_times
@pytest.mark.parametrize('mp_type', ['promp', 'prodmp'])
@pytest.mark.parametrize('max_planning_times', [1, 2, 3])
@pytest.mark.parametrize('sub_segment_steps', [5, 10, 15])
@pytest.mark.parametrize('delay', [0, 0.25, 0.5, 0.75])
@pytest.mark.parametrize('tau', [0.5, 0.75, 1.0])
def test_replanning_with_learn_delay_and_tau(mp_type: str, max_planning_times: int, sub_segment_steps: int,
delay: float, tau: float):
basis_generator_type = 'prodmp' if mp_type == 'prodmp' else 'rbf'
phase_generator_type = 'exp' if mp_type == 'prodmp' else 'linear'
env = fancy_gym.make_bb('toy-v0', [ToyWrapper],
{'replanning_schedule': lambda pos, vel, obs, action, t: t % sub_segment_steps == 0,
'max_planning_times': max_planning_times,
'verbose': 2},
{'trajectory_generator_type': mp_type,
},
{'controller_type': 'motor'},
{'phase_generator_type': phase_generator_type,
'learn_tau': True,
'learn_delay': True
},
{'basis_generator_type': basis_generator_type,
},
seed=SEED)
_ = env.reset()
d = False
planning_times = 0
while not d:
action = env.action_space.sample()
action[0] = tau
action[1] = delay
_, _, d, info = env.step(action)
delay_time_steps = int(np.round(delay / env.dt))
pos = info['positions'].flatten()
vel = info['velocities'].flatten()
# Delay only applies to first planning time
if planning_times == 0:
# Check delay is applied
assert np.all(pos[:max(1, delay_time_steps - 1)] == pos[0])
assert np.all(vel[:max(1, delay_time_steps - 2)] == vel[0])
# Check active trajectory section is different to beginning values
assert np.all(pos[max(1, delay_time_steps):] != pos[0])
assert np.all(vel[max(1, delay_time_steps)] != vel[0])
planning_times += 1
assert planning_times == max_planning_times
@pytest.mark.parametrize('mp_type', ['promp', 'prodmp'])
@pytest.mark.parametrize('max_planning_times', [1, 2, 3, 4])
@pytest.mark.parametrize('sub_segment_steps', [5, 10])
def test_replanning_schedule(mp_type: str, max_planning_times: int, sub_segment_steps: int):
basis_generator_type = 'prodmp' if mp_type == 'prodmp' else 'rbf'
phase_generator_type = 'exp' if mp_type == 'prodmp' else 'linear'
env = fancy_gym.make_bb('toy-v0', [ToyWrapper],
{'max_planning_times': max_planning_times,
'replanning_schedule': lambda pos, vel, obs, action, t: t % sub_segment_steps == 0,
'verbose': 2},
{'trajectory_generator_type': mp_type,
},
{'controller_type': 'motor'},
{'phase_generator_type': phase_generator_type,
'learn_tau': False,
'learn_delay': False
},
{'basis_generator_type': basis_generator_type,
},
seed=SEED)
_ = env.reset()
d = False
for i in range(max_planning_times):
_, _, d, _ = env.step(env.action_space.sample())
assert d
test/utils.py
+1 -1
View File
@@ -49,7 +49,7 @@ def run_env(env_id, iterations=None, seed=0, render=False):
if done: if done:
break break
if not hasattr(env, "replanning_schedule"):
assert done, "Done flag is not True after end of episode." assert done, "Done flag is not True after end of episode."
observations.append(obs) observations.append(obs)
env.close() env.close()