dodox/fancy_gym
1
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity

Merge branch 'master' into readthedocs

Browse Source
This commit is contained in:
Dominik Moritz Roth 2024-01-23 18:04:27 +01:00
commit a9617a392e
8 changed files with 363 additions and 47 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

2
README.md
View File

@ -20,7 +20,7 @@ Built upon the foundation of [Gymnasium](https://gymnasium.farama.org) (a mainta
## Quickstart Guide
| ⚠ We recommend installing `fancy_gym` into a virtual environment as provided by [venv](https://docs.python.org/3/library/venv.html), [Poetry](https://python-poetry.org/) or [Conda](https://docs.conda.io/en/latest/). |
| ----------------------------------------------------------------------------------------------------------------------------------------------- |
| ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
Install via pip [or use an alternative installation method](https://dominik-roth.eu/fancy/guide/installation.html)

2
fancy_gym/envs/__init__.py
View File

@ -291,7 +291,7 @@ register(
)
# Air Hockey environments
for env_mode in ["7dof-hit", "7dof-defend", "3dof-hit", "3dof-defend"]:
for env_mode in ["7dof-hit", "7dof-defend", "3dof-hit", "3dof-defend", "7dof-hit-airhockit2023", "7dof-defend-airhockit2023"]:
register(
id=f'fancy/AirHockey-{env_mode}-v0',
entry_point='fancy_gym.envs.mujoco:AirHockeyEnv',

34
fancy_gym/envs/mujoco/air_hockey/air_hockey_env_wrapper.py
View File

@ -8,9 +8,9 @@ from fancy_gym.envs.mujoco.air_hockey.utils import robot_to_world
from mushroom_rl.core import Environment
class AirHockeyEnv(Environment):
metadata = {"render_modes": ["human"], "render_fps": 50}
metadata = {"render_modes": ["human", "rgb_array"], "render_fps": 50}
def __init__(self, env_mode=None, interpolation_order=3, render_mode=None, **kwargs):
def __init__(self, env_mode=None, interpolation_order=3, render_mode=None, width=1920, height=1080, **kwargs):
"""
Environment Constructor
@ -30,7 +30,10 @@ class AirHockeyEnv(Environment):
"7dof-defend": position.IiwaPositionDefend,
"3dof-hit": position.PlanarPositionHit,
"3dof-defend": position.PlanarPositionDefend
"3dof-defend": position.PlanarPositionDefend,
"7dof-hit-airhockit2023": position.IiwaPositionHitAirhocKIT2023,
"7dof-defend-airhockit2023": position.IiwaPositionDefendAirhocKIT2023,
}
if env_mode not in env_dict:
@ -39,9 +42,25 @@ class AirHockeyEnv(Environment):
if env_mode == "tournament" and type(interpolation_order) != tuple:
interpolation_order = (interpolation_order, interpolation_order)
self.render_mode = render_mode
self.render_human_active = False
# Determine headless mode based on render_mode
headless = self.render_mode == 'rgb_array'
# Prepare viewer_params
viewer_params = kwargs.get('viewer_params', {})
viewer_params.update({'headless': headless, 'width': width, 'height': height})
kwargs['viewer_params'] = viewer_params
self.base_env = env_dict[env_mode](interpolation_order=interpolation_order, **kwargs)
self.env_name = env_mode
self.env_info = self.base_env.env_info
if hasattr(self.base_env, "wrapper_obs_space") and hasattr(self.base_env, "wrapper_act_space"):
self.observation_space = self.base_env.wrapper_obs_space
self.action_space = self.base_env.wrapper_act_space
else:
single_robot_obs_size = len(self.base_env.info.observation_space.low)
if env_mode == "tournament":
self.observation_space = spaces.Box(low=-np.inf, high=np.inf, shape=(2,single_robot_obs_size), dtype=np.float64)
@ -81,9 +100,6 @@ class AirHockeyEnv(Environment):
self.env_info['constraints'] = constraint_list
self.env_info['env_name'] = self.env_name
self.render_mode = render_mode
self.render_human_active = False
super().__init__(self.base_env.info)
def step(self, action):
@ -118,7 +134,13 @@ class AirHockeyEnv(Environment):
return obs, reward, done, False, info
def render(self):
if self.render_mode == 'rgb_array':
return self.base_env.render(record = True)
elif self.render_mode == 'human':
self.render_human_active = True
self.base_env.render()
else:
raise ValueError(f"Unsupported render mode: '{self.render_mode}'")
def reset(self, seed=None, options={}):
self.base_env.seed(seed)

4
fancy_gym/envs/mujoco/air_hockey/position_control_wrapper.py
View File

@ -261,10 +261,14 @@ class PlanarPositionDefend(PositionControlPlanar, three_dof.AirHockeyDefend):
class IiwaPositionHit(PositionControlIIWA, seven_dof.AirHockeyHit):
pass
class IiwaPositionHitAirhocKIT2023(PositionControlIIWA, seven_dof.AirHockeyHitAirhocKIT2023):
pass
class IiwaPositionDefend(PositionControlIIWA, seven_dof.AirHockeyDefend):
pass
class IiwaPositionDefendAirhocKIT2023(PositionControlIIWA, seven_dof.AirHockeyDefendAirhocKIT2023):
pass
class IiwaPositionTournament(PositionControlIIWA, seven_dof.AirHockeyTournament):
pass

4
fancy_gym/envs/mujoco/air_hockey/seven_dof/__init__.py
View File

@ -1,4 +1,4 @@
from .env_base import AirHockeyBase
from .tournament import AirHockeyTournament
from .hit import AirHockeyHit
from .defend import AirHockeyDefend
from .hit import AirHockeyHit, AirHockeyHitAirhocKIT2023
from .defend import AirHockeyDefend, AirHockeyDefendAirhocKIT2023

114
fancy_gym/envs/mujoco/air_hockey/seven_dof/airhockit_base_env.py Normal file
View File

@ -0,0 +1,114 @@
import numpy as np
from gymnasium import spaces
from fancy_gym.envs.mujoco.air_hockey.seven_dof.env_single import AirHockeySingle
from fancy_gym.envs.mujoco.air_hockey.utils import inverse_kinematics, forward_kinematics, jacobian
class AirhocKIT2023BaseEnv(AirHockeySingle):
def __init__(self, noise=False, **kwargs):
super().__init__(**kwargs)
obs_low = np.hstack([[-np.inf] * 37])
obs_high = np.hstack([[np.inf] * 37])
self.wrapper_obs_space = spaces.Box(low=obs_low, high=obs_high, dtype=np.float64)
self.wrapper_act_space = spaces.Box(low=np.repeat(-100., 6), high=np.repeat(100., 6))
self.noise = noise
# We don't need puck yaw observations
def filter_obs(self, obs):
obs = np.hstack([obs[0:2], obs[3:5], obs[6:12], obs[13:19], obs[20:]])
return obs
def add_noise(self, obs):
if not self.noise:
return
obs[self.env_info["puck_pos_ids"]] += np.random.normal(0, 0.001, 3)
obs[self.env_info["puck_vel_ids"]] += np.random.normal(0, 0.1, 3)
def reset(self):
self.last_acceleration = np.repeat(0., 6)
obs = super().reset()
self.add_noise(obs)
self.interp_pos = obs[self.env_info["joint_pos_ids"]][:-1]
self.interp_vel = obs[self.env_info["joint_vel_ids"]][:-1]
self.last_planned_world_pos = self._fk(self.interp_pos)
obs = np.hstack([
obs, self.interp_pos, self.interp_vel, self.last_acceleration, self.last_planned_world_pos
])
return self.filter_obs(obs)
def step(self, action):
action /= 10
new_vel = self.interp_vel + action
jerk = 2 * (new_vel - self.interp_vel - self.last_acceleration * 0.02) / (0.02 ** 2)
new_pos = self.interp_pos + self.interp_vel * 0.02 + (1/2) * self.last_acceleration * (0.02 ** 2) + (1/6) * jerk * (0.02 ** 3)
abs_action = np.vstack([np.hstack([new_pos, 0]), np.hstack([new_vel, 0])])
self.interp_pos = new_pos
self.interp_vel = new_vel
self.last_acceleration += jerk * 0.02
obs, rew, done, info = super().step(abs_action)
self.add_noise(obs)
self.last_planned_world_pos = self._fk(self.interp_pos)
obs = np.hstack([
obs, self.interp_pos, self.interp_vel, self.last_acceleration, self.last_planned_world_pos
])
fatal_rew = self.check_fatal(obs)
if fatal_rew != 0:
return self.filter_obs(obs), fatal_rew, True, info
return self.filter_obs(obs), rew, done, info
def check_constraints(self, constraint_values):
fatal_rew = 0
j_pos_constr = constraint_values["joint_pos_constr"]
if j_pos_constr.max() > 0:
fatal_rew += j_pos_constr.max()
j_vel_constr = constraint_values["joint_vel_constr"]
if j_vel_constr.max() > 0:
fatal_rew += j_vel_constr.max()
ee_constr = constraint_values["ee_constr"]
if ee_constr.max() > 0:
fatal_rew += ee_constr.max()
link_constr = constraint_values["link_constr"]
if link_constr.max() > 0:
fatal_rew += link_constr.max()
return -fatal_rew
def check_fatal(self, obs):
fatal_rew = 0
q = obs[self.env_info["joint_pos_ids"]]
qd = obs[self.env_info["joint_vel_ids"]]
constraint_values_obs = self.env_info["constraints"].fun(q, qd)
fatal_rew += self.check_constraints(constraint_values_obs)
return -fatal_rew
def _fk(self, pos):
res, _ = forward_kinematics(self.env_info["robot"]["robot_model"],
self.env_info["robot"]["robot_data"], pos)
return res.astype(np.float32)
def _ik(self, world_pos, init_q=None):
success, pos = inverse_kinematics(self.env_info["robot"]["robot_model"],
self.env_info["robot"]["robot_data"],
world_pos,
initial_q=init_q)
pos = pos.astype(np.float32)
assert success
return pos
def _jacobian(self, pos):
return jacobian(self.env_info["robot"]["robot_model"],
self.env_info["robot"]["robot_data"],
pos).astype(np.float32)

97
fancy_gym/envs/mujoco/air_hockey/seven_dof/defend.py
View File

@ -1,6 +1,7 @@
import numpy as np
from fancy_gym.envs.mujoco.air_hockey.seven_dof.env_single import AirHockeySingle
from fancy_gym.envs.mujoco.air_hockey.seven_dof.airhockit_base_env import AirhocKIT2023BaseEnv
class AirHockeyDefend(AirHockeySingle):
@ -10,9 +11,7 @@ class AirHockeyDefend(AirHockeySingle):
"""
def __init__(self, gamma=0.99, horizon=500, viewer_params={}):
self.init_velocity_range = (1, 3)
self.start_range = np.array([[0.29, 0.65], [-0.4, 0.4]]) # Table Frame
self.init_ee_range = np.array([[0.60, 1.25], [-0.4, 0.4]]) # Robot Frame
super().__init__(gamma=gamma, horizon=horizon, viewer_params=viewer_params)
def setup(self, obs):
@ -32,7 +31,7 @@ class AirHockeyDefend(AirHockeySingle):
self._write_data("puck_y_vel", puck_vel[1])
self._write_data("puck_yaw_vel", puck_vel[2])
super(AirHockeyDefend, self).setup(obs)
super().setup(obs)
def reward(self, state, action, next_state, absorbing):
return 0
@ -46,6 +45,98 @@ class AirHockeyDefend(AirHockeySingle):
return True
return super().is_absorbing(state)
class AirHockeyDefendAirhocKIT2023(AirhocKIT2023BaseEnv):
def __init__(self, gamma=0.99, horizon=200, viewer_params={}, **kwargs):
super().__init__(gamma=gamma, horizon=horizon, viewer_params=viewer_params, **kwargs)
self.init_velocity_range = (1, 3)
self.start_range = np.array([[0.4, 0.75], [-0.4, 0.4]]) # Table Frame
self._setup_metrics()
def setup(self, obs):
self._setup_metrics()
puck_pos = np.random.rand(2) * (self.start_range[:, 1] - self.start_range[:, 0]) + self.start_range[:, 0]
lin_vel = np.random.uniform(self.init_velocity_range[0], self.init_velocity_range[1])
angle = np.random.uniform(-0.5, 0.5)
puck_vel = np.zeros(3)
puck_vel[0] = -np.cos(angle) * lin_vel
puck_vel[1] = np.sin(angle) * lin_vel
puck_vel[2] = np.random.uniform(-10, 10)
self._write_data("puck_x_pos", puck_pos[0])
self._write_data("puck_y_pos", puck_pos[1])
self._write_data("puck_x_vel", puck_vel[0])
self._write_data("puck_y_vel", puck_vel[1])
self._write_data("puck_yaw_vel", puck_vel[2])
super().setup(obs)
def reset(self, *args):
obs = super().reset()
self.hit_step_flag = False
self.hit_step = False
self.received_hit_reward = False
self.give_reward_next = False
return obs
def _setup_metrics(self):
self.episode_steps = 0
self.has_hit = False
def _simulation_post_step(self):
if not self.has_hit:
self.has_hit = self._check_collision("puck", "robot_1/ee")
super()._simulation_post_step()
def _step_finalize(self):
self.episode_steps += 1
return super()._step_finalize()
def reward(self, state, action, next_state, absorbing):
puck_pos, puck_vel = self.get_puck(next_state)
ee_pos, _ = self.get_ee()
rew = 0.01
if -0.7 < puck_pos[0] <= -0.2 and np.linalg.norm(puck_vel[:2]) < 0.1:
assert absorbing
rew += 70
if self.has_hit and not self.hit_step_flag:
self.hit_step_flag = True
self.hit_step = True
else:
self.hit_step = False
f = lambda puck_vel: 30 + 100 * (100 ** (-0.25 * np.linalg.norm(puck_vel[:2])))
if not self.give_reward_next and not self.received_hit_reward and self.hit_step and ee_pos[0] < puck_pos[0]:
self.hit_this_step = True
if np.linalg.norm(puck_vel[:2]) < 0.1:
return rew + f(puck_vel)
self.give_reward_next = True
return rew
if not self.received_hit_reward and self.give_reward_next:
self.received_hit_reward = True
if puck_vel[0] >= -0.2:
return rew + f(puck_vel)
return rew
else:
return rew
def is_absorbing(self, obs):
puck_pos, puck_vel = self.get_puck(obs)
# If puck is over the middle line and moving towards opponent
if puck_pos[0] > 0 and puck_vel[0] > 0:
return True
if self.episode_steps == self._mdp_info.horizon:
return True
if np.linalg.norm(puck_vel[:2]) < 0.1:
return True
return super().is_absorbing(obs)
if __name__ == '__main__':
env = AirHockeyDefend()

91
fancy_gym/envs/mujoco/air_hockey/seven_dof/hit.py
View File

@ -1,6 +1,7 @@
import numpy as np
from fancy_gym.envs.mujoco.air_hockey.seven_dof.env_single import AirHockeySingle
from fancy_gym.envs.mujoco.air_hockey.seven_dof.airhockit_base_env import AirhocKIT2023BaseEnv
class AirHockeyHit(AirHockeySingle):
@ -14,9 +15,6 @@ class AirHockeyHit(AirHockeySingle):
opponent_agent(Agent, None): Agent which controls the opponent
moving_init(bool, False): If true, initialize the puck with inital velocity.
"""
self.hit_range = np.array([[-0.65, -0.25], [-0.4, 0.4]]) # Table Frame
self.init_velocity_range = (0, 0.5) # Table Frame
super().__init__(gamma=gamma, horizon=horizon, viewer_params=viewer_params)
self.moving_init = moving_init
@ -58,6 +56,93 @@ class AirHockeyHit(AirHockeySingle):
return True
return super(AirHockeyHit, self).is_absorbing(obs)
class AirHockeyHitAirhocKIT2023(AirhocKIT2023BaseEnv):
def __init__(self, gamma=0.99, horizon=500, moving_init=True, viewer_params={}, **kwargs):
super().__init__(gamma=gamma, horizon=horizon, viewer_params=viewer_params, **kwargs)
self.moving_init = moving_init
hit_width = self.env_info['table']['width'] / 2 - self.env_info['puck']['radius'] - \
self.env_info['mallet']['radius'] * 2
self.hit_range = np.array([[-0.7, -0.2], [-hit_width, hit_width]]) # Table Frame
self.init_velocity_range = (0, 0.5) # Table Frame
self.init_ee_range = np.array([[0.60, 1.25], [-0.4, 0.4]]) # Robot Frame
self._setup_metrics()
def reset(self, *args):
obs = super().reset()
self.last_ee_pos = self.last_planned_world_pos.copy()
self.last_ee_pos[0] -= 1.51
return obs
def setup(self, obs):
self._setup_metrics()
puck_pos = np.random.rand(2) * (self.hit_range[:, 1] - self.hit_range[:, 0]) + self.hit_range[:, 0]
self._write_data("puck_x_pos", puck_pos[0])
self._write_data("puck_y_pos", puck_pos[1])
if self.moving_init:
lin_vel = np.random.uniform(self.init_velocity_range[0], self.init_velocity_range[1])
angle = np.random.uniform(-np.pi / 2 - 0.1, np.pi / 2 + 0.1)
puck_vel = np.zeros(3)
puck_vel[0] = -np.cos(angle) * lin_vel
puck_vel[1] = np.sin(angle) * lin_vel
puck_vel[2] = np.random.uniform(-2, 2)
self._write_data("puck_x_vel", puck_vel[0])
self._write_data("puck_y_vel", puck_vel[1])
self._write_data("puck_yaw_vel", puck_vel[2])
super().setup(obs)
def _setup_metrics(self):
self.episode_steps = 0
self.has_scored = False
def _step_finalize(self):
cur_obs = self._create_observation(self.obs_helper._build_obs(self._data))
puck_pos, _ = self.get_puck(cur_obs) # world frame [x, y, z] and [x', y', z']
if not self.has_scored:
boundary = np.array([self.env_info['table']['length'], self.env_info['table']['width']]) / 2
self.has_scored = np.any(np.abs(puck_pos[:2]) > boundary) and puck_pos[0] > 0
self.episode_steps += 1
return super()._step_finalize()
def reward(self, state, action, next_state, absorbing):
rew = 0
puck_pos, puck_vel = self.get_puck(next_state)
ee_pos, _ = self.get_ee()
ee_vel = (ee_pos - self.last_ee_pos) / 0.02
self.last_ee_pos = ee_pos
if puck_vel[0] < 0.25 and puck_pos[0] < 0:
ee_puck_dir = (puck_pos - ee_pos)[:2]
ee_puck_dir = ee_puck_dir / np.linalg.norm(ee_puck_dir)
rew += 1 * max(0, np.dot(ee_puck_dir, ee_vel[:2]))
else:
rew += 10 * np.linalg.norm(puck_vel[:2])
if self.has_scored:
rew += 2000 + 5000 * np.linalg.norm(puck_vel[:2])
return rew
def is_absorbing(self, obs):
puck_pos, puck_vel = self.get_puck(obs)
# Stop if the puck bounces back on the opponents wall
if puck_pos[0] > 0 and puck_vel[0] < 0:
return True
if self.has_scored:
return True
if self.episode_steps == self._mdp_info.horizon:
return True
return super().is_absorbing(obs)
if __name__ == '__main__':
env = AirHockeyHit(moving_init=True)