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added all metaworld tasks as ProMP

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This commit is contained in:
ottofabian 2021-08-20 14:23:33 +02:00
parent cb603859d9
commit c39877ece0
8 changed files with 344 additions and 35 deletions
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103
alr_envs/__init__.py
View File

@ -316,7 +316,6 @@ register(
"alpha_phase": 2,
"bandwidth_factor": 2,
"policy_type": "motor",
"weights_scale": 50,
"goal_scale": 0.1,
"policy_kwargs": {
"p_gains": 50,
@ -340,7 +339,6 @@ register(
"duration": 2,
"width": 0.025,
"policy_type": "motor",
"weights_scale": 1,
"zero_start": True,
"policy_kwargs": {
"p_gains": 50,
@ -828,6 +826,7 @@ register(
"duration": 2,
"post_traj_time": 0,
"width": 0.02,
"zero_start": True,
"policy_type": "motor",
"policy_kwargs": {
"p_gains": 1.,
@ -849,6 +848,7 @@ register(
"duration": 1,
"post_traj_time": 0,
"width": 0.02,
"zero_start": True,
"policy_type": "motor",
"policy_kwargs": {
"p_gains": .6,
@ -870,6 +870,7 @@ register(
"duration": 2,
"post_traj_time": 0,
"width": 0.02,
"zero_start": True,
"policy_type": "position"
}
}
@ -887,6 +888,7 @@ register(
"duration": 2,
"post_traj_time": 0,
"width": 0.02,
"zero_start": True,
"policy_type": "position"
}
}
@ -904,6 +906,7 @@ register(
"duration": 2,
"post_traj_time": 0,
"width": 0.02,
"zero_start": True,
"policy_type": "position"
}
}
@ -921,25 +924,109 @@ register(
"duration": 2,
"post_traj_time": 0,
"width": 0.02,
"zero_start": True,
"policy_type": "position"
}
}
)
register(
id='ButtonPressDetPMP-v2',
# MetaWorld
goal_change_envs = ["assembly-v2", "pick-out-of-hole-v2", "plate-slide-v2", "plate-slide-back-v2",
]
for env_id in goal_change_envs:
env_id_split = env_id.split("-")
name = "".join([s.capitalize() for s in env_id_split[:-1]])
register(
id=f'{name}DetPMP-{env_id_split[-1]}',
entry_point='alr_envs.utils.make_env_helpers:make_detpmp_env_helper',
kwargs={
"name": "button-press-v2",
"wrappers": [meta.button_press.MPWrapper],
"name": env_id,
"wrappers": [meta.goal_change.MPWrapper],
"mp_kwargs": {
"num_dof": 4,
"num_basis": 5,
"duration": 6.25,
"post_traj_time": 0,
"width": 0.025,
"policy_type": "position"
"zero_start": True,
"policy_type": "metaworld",
}
}
)
)
object_change_envs = ["bin-picking-v2", "hammer-v2", "sweep-into-v2"]
for env_id in object_change_envs:
env_id_split = env_id.split("-")
name = "".join([s.capitalize() for s in env_id_split[:-1]])
register(
id=f'{name}DetPMP-{env_id_split[-1]}',
entry_point='alr_envs.utils.make_env_helpers:make_detpmp_env_helper',
kwargs={
"name": env_id,
"wrappers": [meta.object_change.MPWrapper],
"mp_kwargs": {
"num_dof": 4,
"num_basis": 5,
"duration": 6.25,
"post_traj_time": 0,
"width": 0.025,
"zero_start": True,
"policy_type": "metaworld",
}
}
)
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",
"coffee-push-v2", "dial-turn-v2", "disassemble-v2", "door-close-v2",
"door-lock-v2", "door-open-v2", "door-unlock-v2", "hand-insert-v2",
"drawer-close-v2", "drawer-open-v2", "faucet-open-v2", "faucet-close-v2",
"handle-press-side-v2", "handle-press-v2", "handle-pull-side-v2",
"handle-pull-v2", "lever-pull-v2", "peg-insert-side-v2", "pick-place-wall-v2",
"reach-v2", "push-back-v2", "push-v2", "pick-place-v2", "peg-unplug-side-v2",
"soccer-v2", "stick-push-v2", "stick-pull-v2", "push-wall-v2", "reach-wall-v2",
"shelf-place-v2", "sweep-v2", "window-open-v2", "window-close-v2"
]
for env_id in goal_and_object_change_envs:
env_id_split = env_id.split("-")
name = "".join([s.capitalize() for s in env_id_split[:-1]])
register(
id=f'{name}DetPMP-{env_id_split[-1]}',
entry_point='alr_envs.utils.make_env_helpers:make_detpmp_env_helper',
kwargs={
"name": env_id,
"wrappers": [meta.goal_and_object_change.MPWrapper],
"mp_kwargs": {
"num_dof": 4,
"num_basis": 5,
"duration": 6.25,
"post_traj_time": 0,
"width": 0.025,
"zero_start": True,
"policy_type": "metaworld",
}
}
)
goal_and_endeffector_change_envs = ["basketball-v2"]
for env_id in goal_and_endeffector_change_envs:
env_id_split = env_id.split("-")
name = "".join([s.capitalize() for s in env_id_split[:-1]])
register(
id=f'{name}DetPMP-{env_id_split[-1]}',
entry_point='alr_envs.utils.make_env_helpers:make_detpmp_env_helper',
kwargs={
"name": env_id,
"wrappers": [meta.goal_and_endeffector_change.MPWrapper],
"mp_kwargs": {
"num_dof": 4,
"num_basis": 5,
"duration": 6.25,
"post_traj_time": 0,
"width": 0.025,
"zero_start": True,
"policy_type": "metaworld",
}
}
)

11
alr_envs/examples/examples_general.py
View File

@ -80,20 +80,21 @@ def example_async(env_id="alr_envs:HoleReacher-v0", n_cpu=4, seed=int('533D', 16
rewards[done] = 0
# do not return values above threshold
return *map(lambda v: np.stack(v)[:n_samples], buffer.values()),
return (*map(lambda v: np.stack(v)[:n_samples], buffer.values()),)
if __name__ == '__main__':
render = False
render = True
# Basic gym task
example_general("Pendulum-v0", seed=10, iterations=200, render=render)
#
# # Basis task from framework
example_general("alr_envs:HoleReacher-v0", seed=10, iterations=200, render=render)
#
# # OpenAI Mujoco task
example_general("HalfCheetah-v2", seed=10, render=render)
#
# # Mujoco task from framework
example_general("alr_envs:ALRReacher-v0", seed=10, iterations=200, render=render)

20
alr_envs/examples/pd_control_gain_tuning.py
View File

@ -1,30 +1,30 @@
import numpy as np
from matplotlib import pyplot as plt
from alr_envs import dmc
from alr_envs import dmc, meta
from alr_envs.utils.make_env_helpers import make_detpmp_env
# This might work for some environments, however, please verify either way the correct trajectory information
# for your environment are extracted below
SEED = 10
env_id = "cartpole-swingup"
wrappers = [dmc.suite.cartpole.MPWrapper]
env_id = "ball_in_cup-catch"
wrappers = [dmc.ball_in_cup.MPWrapper]
mp_kwargs = {
"num_dof": 1,
"num_basis": 5,
"num_dof": 2,
"num_basis": 10,
"duration": 2,
"width": 0.025,
"policy_type": "motor",
"weights_scale": 0.2,
"weights_scale": 1,
"zero_start": True,
"policy_kwargs": {
"p_gains": 10,
"d_gains": 10 # a good starting point is the sqrt of p_gains
"p_gains": 1,
"d_gains": 1
}
}
kwargs = dict(time_limit=2, episode_length=200)
kwargs = dict(time_limit=2, episode_length=100)
env = make_detpmp_env(env_id, wrappers, seed=SEED, mp_kwargs=mp_kwargs,
**kwargs)
@ -35,7 +35,6 @@ pos, vel = env.mp_rollout(env.action_space.sample())
base_shape = env.full_action_space.shape
actual_pos = np.zeros((len(pos), *base_shape))
actual_pos_ball = np.zeros((len(pos), *base_shape))
actual_vel = np.zeros((len(pos), *base_shape))
act = np.zeros((len(pos), *base_shape))
@ -46,7 +45,6 @@ for t, pos_vel in enumerate(zip(pos, vel)):
act[t, :] = actions
# TODO verify for your environment
actual_pos[t, :] = env.current_pos
# actual_pos_ball[t, :] = env.physics.data.qpos[2:]
actual_vel[t, :] = env.current_vel
plt.figure(figsize=(15, 5))

2
alr_envs/meta/__init__.py
View File

@ -1 +1 @@
from alr_envs.meta import button_press
from alr_envs.meta import goal_and_object_change, goal_and_endeffector_change, goal_change, object_change

68
alr_envs/meta/goal_and_endeffector_change.py Normal file
View File

@ -0,0 +1,68 @@
from typing import Tuple, Union
import numpy as np
from mp_env_api import MPEnvWrapper
class MPWrapper(MPEnvWrapper):
"""
This Wrapper is for environments where merely the goal changes in the beginning
and no secondary objects or end effectors are altered at the start of an episode.
You can verify this by executing the code below for your environment id and check if the output is non-zero
at the same indices.
```python
import alr_envs
env = alr_envs.make(env_id, 1)
print(env.reset() - env.reset())
array([ !=0 , !=0 , !=0 , 0. , 0.,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , !=0 , !=0 ,
!=0 , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , !=0 , !=0 , !=0])
```
"""
@property
def active_obs(self):
# This structure is the same for all metaworld environments.
# Only the observations which change could differ
return np.hstack([
# Current observation
[True] * 3, # end-effector position
[False] * 1, # normalized gripper open distance
[False] * 3, # main object position
[False] * 4, # main object quaternion
[False] * 3, # secondary object position
[False] * 4, # secondary object quaternion
# Previous observation
# TODO: Include previous values? According to their source they might be wrong for the first iteration.
[False] * 3, # previous end-effector position
[False] * 1, # previous normalized gripper open distance
[False] * 3, # previous main object position
[False] * 4, # previous main object quaternion
[False] * 3, # previous second object position
[False] * 4, # previous second object quaternion
# Goal
[True] * 3, # goal position
])
@property
def current_pos(self) -> Union[float, int, np.ndarray]:
r_close = self.env.data.get_joint_qpos("r_close")
return np.hstack([self.env.data.mocap_pos.flatten(), r_close])
@property
def current_vel(self) -> Union[float, int, np.ndarray, Tuple]:
raise NotImplementedError("Velocity cannot be retrieved.")
@property
def goal_pos(self) -> Union[float, int, np.ndarray, Tuple]:
raise ValueError("Goal position is not available and has to be learnt based on the environment.")
@property
def dt(self) -> Union[float, int]:
return self.env.dt

19
alr_envs/meta/button_press.py → alr_envs/meta/goal_and_object_change.py
View File

@ -6,6 +6,25 @@ from mp_env_api import MPEnvWrapper
class MPWrapper(MPEnvWrapper):
"""
This Wrapper is for environments where merely the goal changes in the beginning
and no secondary objects or end effectors are altered at the start of an episode.
You can verify this by executing the code below for your environment id and check if the output is non-zero
at the same indices.
```python
import alr_envs
env = alr_envs.make(env_id, 1)
print(env.reset() - env.reset())
array([ 0. , 0. , 0. , 0. , !=0,
!=0 , !=0 , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , !=0 , !=0 , !=0 ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , !=0 , !=0 , !=0])
```
"""
@property
def active_obs(self):

68
alr_envs/meta/goal_change.py Normal file
View File

@ -0,0 +1,68 @@
from typing import Tuple, Union
import numpy as np
from mp_env_api import MPEnvWrapper
class MPWrapper(MPEnvWrapper):
"""
This Wrapper is for environments where merely the goal changes in the beginning
and no secondary objects or end effectors are altered at the start of an episode.
You can verify this by executing the code below for your environment id and check if the output is non-zero
at the same indices.
```python
import alr_envs
env = alr_envs.make(env_id, 1)
print(env.reset() - env.reset())
array([ 0. , 0. , 0. , 0. , 0,
0 , 0 , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0 , 0 , 0 ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , !=0 , !=0 , !=0])
```
"""
@property
def active_obs(self):
# This structure is the same for all metaworld environments.
# Only the observations which change could differ
return np.hstack([
# Current observation
[False] * 3, # end-effector position
[False] * 1, # normalized gripper open distance
[False] * 3, # main object position
[False] * 4, # main object quaternion
[False] * 3, # secondary object position
[False] * 4, # secondary object quaternion
# Previous observation
# TODO: Include previous values? According to their source they might be wrong for the first iteration.
[False] * 3, # previous end-effector position
[False] * 1, # previous normalized gripper open distance
[False] * 3, # previous main object position
[False] * 4, # previous main object quaternion
[False] * 3, # previous second object position
[False] * 4, # previous second object quaternion
# Goal
[True] * 3, # goal position
])
@property
def current_pos(self) -> Union[float, int, np.ndarray]:
r_close = self.env.data.get_joint_qpos("r_close")
return np.hstack([self.env.data.mocap_pos.flatten(), r_close])
@property
def current_vel(self) -> Union[float, int, np.ndarray, Tuple]:
raise NotImplementedError("Velocity cannot be retrieved.")
@property
def goal_pos(self) -> Union[float, int, np.ndarray, Tuple]:
raise ValueError("Goal position is not available and has to be learnt based on the environment.")
@property
def dt(self) -> Union[float, int]:
return self.env.dt

68
alr_envs/meta/object_change.py Normal file
View File

@ -0,0 +1,68 @@
from typing import Tuple, Union
import numpy as np
from mp_env_api import MPEnvWrapper
class MPWrapper(MPEnvWrapper):
"""
This Wrapper is for environments where merely the goal changes in the beginning
and no secondary objects or end effectors are altered at the start of an episode.
You can verify this by executing the code below for your environment id and check if the output is non-zero
at the same indices.
```python
import alr_envs
env = alr_envs.make(env_id, 1)
print(env.reset() - env.reset())
array([ 0. , 0. , 0. , 0. , !=0 ,
!=0 , !=0 , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0 , 0 , 0 ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0.])
```
"""
@property
def active_obs(self):
# This structure is the same for all metaworld environments.
# Only the observations which change could differ
return np.hstack([
# Current observation
[False] * 3, # end-effector position
[False] * 1, # normalized gripper open distance
[False] * 3, # main object position
[False] * 4, # main object quaternion
[False] * 3, # secondary object position
[False] * 4, # secondary object quaternion
# Previous observation
# TODO: Include previous values? According to their source they might be wrong for the first iteration.
[False] * 3, # previous end-effector position
[False] * 1, # previous normalized gripper open distance
[False] * 3, # previous main object position
[False] * 4, # previous main object quaternion
[False] * 3, # previous second object position
[False] * 4, # previous second object quaternion
# Goal
[True] * 3, # goal position
])
@property
def current_pos(self) -> Union[float, int, np.ndarray]:
r_close = self.env.data.get_joint_qpos("r_close")
return np.hstack([self.env.data.mocap_pos.flatten(), r_close])
@property
def current_vel(self) -> Union[float, int, np.ndarray, Tuple]:
raise NotImplementedError("Velocity cannot be retrieved.")
@property
def goal_pos(self) -> Union[float, int, np.ndarray, Tuple]:
raise ValueError("Goal position is not available and has to be learnt based on the environment.")
@property
def dt(self) -> Union[float, int]:
return self.env.dt