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lerobot
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Package xarm

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Simon Alibert 2024-03-29 13:59:09 +01:00
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envs/sim_xarm/README.md Normal file
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# xArm environment for LeRobot

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fpx = ["olefile"]
mic = ["olefile"]
tests = ["check-manifest", "coverage", "defusedxml", "markdown2", "olefile", "packaging", "pyroma", "pytest", "pytest-cov", "pytest-timeout"]
typing = ["typing-extensions"]
xmp = ["defusedxml"]
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name = "pyopengl"
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description = "Standard OpenGL bindings for Python"
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python-versions = "*"
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description = "Backported and Experimental Type Hints for Python 3.8+"
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python-versions = ">=3.8"
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[metadata]
lock-version = "2.0"
python-versions = "^3.10"
content-hash = "165d82035aade2abad497b32e156ec18d8ebc6c57a36376c3351b593c6889f22"

34
envs/sim_xarm/pyproject.toml Normal file
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[tool.poetry]
name = "sim_xarm"
version = "0.1.0"
description = "xArm environment for LeRobot"
authors = [
"Rémi Cadène <re.cadene@gmail.com>",
]
maintainers = [
"Alexander Soare <alexander.soare159@gmail.com>",
"Quentin Gallouédec <quentin.gallouedec@ec-lyon.fr>",
"Simon Alibert <alibert.sim@gmail.com>",
]
readme = "README.md"
license = "Apache-2.0"
classifiers=[
"Development Status :: 3 - Alpha",
"Intended Audience :: Developers",
"Topic :: Software Development :: Build Tools",
"License :: OSI Approved :: Apache Software License",
"Programming Language :: Python :: 3.10",
]
packages = [{include = "xarm"}]
[tool.poetry.dependencies]
python = "^3.10"
mujoco = "^2.3.7"
gymnasium = "^0.29.1"
gymnasium-robotics = "^1.2.4"
[build-system]
requires = ["poetry-core"]
build-backend = "poetry.core.masonry.api"

166
envs/sim_xarm/xarm/__init__.py Normal file
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from collections import OrderedDict, deque
import gymnasium as gym
import numpy as np
from gymnasium.wrappers import TimeLimit
from xarm.tasks.base import Base as Base
from xarm.tasks.lift import Lift
from xarm.tasks.peg_in_box import PegInBox
from xarm.tasks.push import Push
from xarm.tasks.reach import Reach
TASKS = OrderedDict(
(
(
"reach",
{
"env": Reach,
"action_space": "xyz",
"episode_length": 50,
"description": "Reach a target location with the end effector",
},
),
(
"push",
{
"env": Push,
"action_space": "xyz",
"episode_length": 50,
"description": "Push a cube to a target location",
},
),
(
"peg_in_box",
{
"env": PegInBox,
"action_space": "xyz",
"episode_length": 50,
"description": "Insert a peg into a box",
},
),
(
"lift",
{
"env": Lift,
"action_space": "xyzw",
"episode_length": 50,
"description": "Lift a cube above a height threshold",
},
),
)
)
class SimXarmWrapper(gym.Wrapper):
"""
A wrapper for the SimXarm environments. This wrapper is used to
convert the action and observation spaces to the correct format.
"""
def __init__(self, env, task, obs_mode, image_size, action_repeat, frame_stack=1, channel_last=False):
super().__init__(env)
self._env = env
self.obs_mode = obs_mode
self.image_size = image_size
self.action_repeat = action_repeat
self.frame_stack = frame_stack
self._frames = deque([], maxlen=frame_stack)
self.channel_last = channel_last
self._max_episode_steps = task["episode_length"] // action_repeat
image_shape = (
(image_size, image_size, 3 * frame_stack)
if channel_last
else (3 * frame_stack, image_size, image_size)
)
if obs_mode == "state":
self.observation_space = env.observation_space["observation"]
elif obs_mode == "rgb":
self.observation_space = gym.spaces.Box(low=0, high=255, shape=image_shape, dtype=np.uint8)
elif obs_mode == "all":
self.observation_space = gym.spaces.Dict(
state=gym.spaces.Box(low=-np.inf, high=np.inf, shape=(4,), dtype=np.float32),
rgb=gym.spaces.Box(low=0, high=255, shape=image_shape, dtype=np.uint8),
)
else:
raise ValueError(f"Unknown obs_mode {obs_mode}. Must be one of [rgb, all, state]")
self.action_space = gym.spaces.Box(low=-1.0, high=1.0, shape=(len(task["action_space"]),))
self.action_padding = np.zeros(4 - len(task["action_space"]), dtype=np.float32)
if "w" not in task["action_space"]:
self.action_padding[-1] = 1.0
def _render_obs(self):
obs = self.render(mode="rgb_array", width=self.image_size, height=self.image_size)
if not self.channel_last:
obs = obs.transpose(2, 0, 1)
return obs.copy()
def _update_frames(self, reset=False):
pixels = self._render_obs()
self._frames.append(pixels)
if reset:
for _ in range(1, self.frame_stack):
self._frames.append(pixels)
assert len(self._frames) == self.frame_stack
def transform_obs(self, obs, reset=False):
if self.obs_mode == "state":
return obs["observation"]
elif self.obs_mode == "rgb":
self._update_frames(reset=reset)
rgb_obs = np.concatenate(list(self._frames), axis=-1 if self.channel_last else 0)
return rgb_obs
elif self.obs_mode == "all":
self._update_frames(reset=reset)
rgb_obs = np.concatenate(list(self._frames), axis=-1 if self.channel_last else 0)
return OrderedDict((("rgb", rgb_obs), ("state", self.robot_state)))
else:
raise ValueError(f"Unknown obs_mode {self.obs_mode}. Must be one of [rgb, all, state]")
def reset(self):
return self.transform_obs(self._env.reset(), reset=True)
def step(self, action):
action = np.concatenate([action, self.action_padding])
reward = 0.0
for _ in range(self.action_repeat):
obs, r, done, info = self._env.step(action)
reward += r
return self.transform_obs(obs), reward, done, info
def render(self, mode="rgb_array", width=384, height=384, **kwargs):
return self._env.render(mode, width=width, height=height)
@property
def state(self):
return self._env.robot_state
def make(task, obs_mode="state", image_size=84, action_repeat=1, frame_stack=1, channel_last=False, seed=0):
"""
Create a new environment.
Args:
task (str): The task to create an environment for. Must be one of:
- 'reach'
- 'push'
- 'peg-in-box'
- 'lift'
obs_mode (str): The observation mode to use. Must be one of:
- 'state': Only state observations
- 'rgb': RGB images
- 'all': RGB images and state observations
image_size (int): The size of the image observations
action_repeat (int): The number of times to repeat the action
seed (int): The random seed to use
Returns:
gym.Env: The environment
"""
if task not in TASKS:
raise ValueError(f"Unknown task {task}. Must be one of {list(TASKS.keys())}")
env = TASKS[task]["env"]()
env = TimeLimit(env, TASKS[task]["episode_length"])
env = SimXarmWrapper(env, TASKS[task], obs_mode, image_size, action_repeat, frame_stack, channel_last)
env.seed(seed)
return env

0
envs/sim_xarm/xarm/tasks/__init__.py Normal file
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53
envs/sim_xarm/xarm/tasks/assets/lift.xml Normal file
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<?xml version="1.0" encoding="utf-8"?>
<mujoco>
<compiler angle="radian" coordinate="local" meshdir="mesh" texturedir="texture"></compiler>
<size nconmax="2000" njmax="500"/>
<option timestep="0.002">
<flag warmstart="enable"></flag>
</option>
<include file="shared.xml"></include>
<worldbody>
<body name="floor0" pos="0 0 0">
<geom name="floorgeom0" pos="1.2 -2.0 0" size="20.0 20.0 1" type="plane" condim="3" material="floor_mat"></geom>
</body>
<include file="xarm.xml"></include>
<body pos="0.75 0 0.6325" name="pedestal0">
<geom name="pedestalgeom0" size="0.1 0.1 0.01" pos="0.32 0.27 0" type="box" mass="2000" material="pedestal_mat"></geom>
<site pos="0.30 0.30 0" size="0.075 0.075 0.002" type="box" name="robotmountsite0" rgba="0.55 0.54 0.53 1" />
</body>
<body pos="1.5 0.075 0.3425" name="table0">
<geom name="tablegeom0" size="0.3 0.6 0.2" pos="0 0 0" type="box" material="table_mat" density="2000" friction="1 1 1"></geom>
</body>
<body name="object" pos="1.405 0.3 0.58625">
<joint name="object_joint0" type="free" limited="false"></joint>
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<?xml version="1.0" encoding="utf-8"?>
<mujoco>
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<body name="B1" pos="0 0 0.04" euler="0 3.14 0 ">
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</body>
</body>
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<light directional="true" ambient="0.1 0.1 0.1" diffuse="0 0 0" specular="0 0 0" castshadow="false" pos="0 -4 4" dir="0 1 -0.1" name="light1"></light>
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<joint joint1="left_inner_knuckle_joint" joint2="right_inner_knuckle_joint"></joint>
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<?xml version="1.0" encoding="utf-8"?>
<mujoco>
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<worldbody>
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</body>
<include file="xarm.xml"></include>
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<geom name="pedestalgeom0" size="0.1 0.1 0.01" pos="0.32 0.27 0" type="box" mass="2000" material="pedestal_mat"></geom>
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</body>
<body name="object" pos="1.655 0.3 0.68">
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<site name="object_site" pos="0 0 0" size="0.024 0.024 0.024" rgba="0 0 0 0" type="box"></site>
</body>
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<light directional="true" ambient="0.1 0.1 0.1" diffuse="0 0 0" specular="0 0 0" castshadow="false" pos="0 -4 4" dir="0 1 -0.1" name="light1"></light>
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<light pos="0 0 2" dir="0.2 0.2 -0.8" directional="true" diffuse="0.3 0.3 0.3" castshadow="false" name="light3"></light>
<camera fovy="50" name="camera0" pos="0.9559 1.0 1.1" euler="-1.1 -0.6 3.4" />
</worldbody>
<equality>
<connect body2="left_finger" body1="left_inner_knuckle" anchor="0.0 0.035 0.042" solimp="0.9 0.95 0.001 0.5 2" solref="0.0002 1.0" ></connect>
<connect body2="right_finger" body1="right_inner_knuckle" anchor="0.0 -0.035 0.042" solimp="0.9 0.95 0.001 0.5 2" solref="0.0002 1.0" ></connect>
<joint joint1="left_inner_knuckle_joint" joint2="right_inner_knuckle_joint"></joint>
</equality>
<actuator>
<motor ctrllimited="true" ctrlrange="-1.0 1.0" joint="left_inner_knuckle_joint" gear="200.0"/>
<motor ctrllimited="true" ctrlrange="-1.0 1.0" joint="right_inner_knuckle_joint" gear="200.0"/>
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</mujoco>

48
envs/sim_xarm/xarm/tasks/assets/reach.xml Normal file
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<?xml version="1.0" encoding="utf-8"?>
<mujoco>
<compiler angle="radian" coordinate="local" meshdir="mesh" texturedir="texture"></compiler>
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<include file="shared.xml"></include>
<worldbody>
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<body pos="1.5 0.075 0.3425" name="table0">
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</body>
<light directional="true" ambient="0.1 0.1 0.1" diffuse="0 0 0" specular="0 0 0" castshadow="false" pos="1.65 0 10" dir="-0.57 -0.57 -0.57" name="light0"></light>
<light directional="true" ambient="0.1 0.1 0.1" diffuse="0 0 0" specular="0 0 0" castshadow="false" pos="0 -4 4" dir="0 1 -0.1" name="light1"></light>
<light directional="true" ambient="0.05 0.05 0.05" diffuse="0 0 0" specular="0 0 0" castshadow="false" pos="2.13 1.6 2.5" name="light2"></light>
<light pos="0 0 2" dir="0.2 0.2 -0.8" directional="true" diffuse="0.3 0.3 0.3" castshadow="false" name="light3"></light>
<camera fovy="50" name="camera0" pos="0.9559 1.0 1.1" euler="-1.1 -0.6 3.4" />
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<equality>
<connect body2="left_finger" body1="left_inner_knuckle" anchor="0.0 0.035 0.042" solimp="0.9 0.95 0.001 0.5 2" solref="0.0002 1.0" ></connect>
<connect body2="right_finger" body1="right_inner_knuckle" anchor="0.0 -0.035 0.042" solimp="0.9 0.95 0.001 0.5 2" solref="0.0002 1.0" ></connect>
<joint joint1="left_inner_knuckle_joint" joint2="right_inner_knuckle_joint"></joint>
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<motor ctrllimited="true" ctrlrange="-1.0 1.0" joint="right_inner_knuckle_joint" gear="200.0"/>
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</mujoco>

51
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<mujoco>
<asset>
<texture type="skybox" builtin="gradient" rgb1="0.0 0.0 0.0" rgb2="0.0 0.0 0.0" width="32" height="32"></texture>
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<material name="pedestal_mat" specular="0.35" shininess="0.5" reflectance="0" rgba="0.705 0.585 0.405 1"></material>
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<material name="robot0:head_mat" shininess="0.03" specular="0.4" reflectance="0"></material>
<material name="robot0:torso_mat" shininess="0.03" specular="0.4" reflectance="0"></material>
<material name="robot0:base_mat" shininess="0.03" specular="0.4" reflectance="0"></material>
<mesh name="link_base" file="link_base.stl" />
<mesh name="link1" file="link1.stl" />
<mesh name="link2" file="link2.stl" />
<mesh name="link3" file="link3.stl" />
<mesh name="link4" file="link4.stl" />
<mesh name="link5" file="link5.stl" />
<mesh name="link6" file="link6.stl" />
<mesh name="link7" file="link7.stl" />
<mesh name="base_link" file="base_link.stl" />
<mesh name="left_outer_knuckle" file="left_outer_knuckle.stl" />
<mesh name="left_finger" file="left_finger.stl" />
<mesh name="left_inner_knuckle" file="left_inner_knuckle.stl" />
<mesh name="right_outer_knuckle" file="right_outer_knuckle.stl" />
<mesh name="right_finger" file="right_finger.stl" />
<mesh name="right_inner_knuckle" file="right_inner_knuckle.stl" />
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<default class="robot0:grey">
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</default>
</default>
</mujoco>

88
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<mujoco model="xarm7">
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<geom conaffinity="0" contype="0" pos="0 0 0" rgba="0 0.5 0 0" size="0.005 0.005 0.005" type="box"></geom>
<geom conaffinity="0" contype="0" pos="0 0 0" rgba="0.5 0 0 0" size="1 0.005 0.005" type="box"></geom>
<geom conaffinity="0" contype="0" pos="0 0 0" rgba="0 0 0.5 0" size="0.005 1 0.001" type="box"></geom>
<geom conaffinity="0" contype="0" pos="0 0 0" rgba="0.5 0.5 0 0" size="0.005 0.005 1" type="box"></geom>
</body>
<body name="link0" pos="1.09 0.28 0.655">
<geom name="bb" type="mesh" mesh="link_base" material="robot0:base_mat" rgba="1 1 1 1"/>
<body name="link1" pos="0 0 0.267">
<inertial pos="-0.0042142 0.02821 -0.0087788" quat="0.917781 -0.277115 0.0606681 0.277858" mass="0.42603" diaginertia="0.00144551 0.00137757 0.000823511" />
<joint name="joint1" pos="0 0 0" axis="0 0 1" limited="true" range="-6.28319 6.28319" damping="10" frictionloss="1" />
<geom name="j1" type="mesh" mesh="link1" material="robot0:arm_mat" rgba="1 1 1 1"/>
<body name="link2" pos="0 0 0" quat="0.707105 -0.707108 0 0">
<inertial pos="-3.3178e-05 -0.12849 0.026337" quat="0.447793 0.894132 -0.00224061 0.00218314" mass="0.56095" diaginertia="0.00319151 0.00311598 0.000980804" />
<joint name="joint2" pos="0 0 0" axis="0 0 1" limited="true" range="-2.059 2.0944" damping="10" frictionloss="1" />
<geom name="j2" type="mesh" mesh="link2" material="robot0:head_mat" rgba="1 1 1 1"/>
<body name="link3" pos="0 -0.293 0" quat="0.707105 0.707108 0 0">
<inertial pos="0.04223 -0.023258 -0.0096674" quat="0.883205 0.339803 0.323238 0.000542237" mass="0.44463" diaginertia="0.00133227 0.00119126 0.000780475" />
<joint name="joint3" pos="0 0 0" axis="0 0 1" limited="true" range="-6.28319 6.28319" damping="5" frictionloss="1" />
<geom name="j3" type="mesh" mesh="link3" material="robot0:gripper_mat" rgba="1 1 1 1"/>
<body name="link4" pos="0.0525 0 0" quat="0.707105 0.707108 0 0">
<inertial pos="0.067148 -0.10732 0.024479" quat="0.0654142 0.483317 -0.738663 0.465298" mass="0.52387" diaginertia="0.00288984 0.00282705 0.000894409" />
<joint name="joint4" pos="0 0 0" axis="0 0 1" limited="true" range="-0.19198 3.927" damping="5" frictionloss="1" />
<geom name="j4" type="mesh" mesh="link4" material="robot0:arm_mat" rgba="1 1 1 1"/>
<body name="link5" pos="0.0775 -0.3425 0" quat="0.707105 0.707108 0 0">
<inertial pos="-0.00023397 0.036705 -0.080064" quat="0.981064 -0.19003 0.00637998 0.0369004" mass="0.18554" diaginertia="0.00099553 0.000988613 0.000247126" />
<joint name="joint5" pos="0 0 0" axis="0 0 1" limited="true" range="-6.28319 6.28319" damping="5" frictionloss="1" />
<geom name="j5" type="mesh" material="robot0:gripper_mat" rgba="1 1 1 1" mesh="link5" />
<body name="link6" pos="0 0 0" quat="0.707105 0.707108 0 0">
<inertial pos="0.058911 0.028469 0.0068428" quat="-0.188705 0.793535 0.166088 0.554173" mass="0.31344" diaginertia="0.000827892 0.000768871 0.000386708" />
<joint name="joint6" pos="0 0 0" axis="0 0 1" limited="true" range="-1.69297 3.14159" damping="2" frictionloss="1" />
<geom name="j6" type="mesh" material="robot0:gripper_mat" rgba="1 1 1 1" mesh="link6" />
<body name="link7" pos="0.076 0.097 0" quat="0.707105 -0.707108 0 0">
<inertial pos="-0.000420033 -0.00287433 0.0257078" quat="0.999372 -0.0349129 -0.00605634 0.000551744" mass="0.85624" diaginertia="0.00137671 0.00118744 0.000514968" />
<joint name="joint7" pos="0 0 0" axis="0 0 1" limited="true" range="-6.28319 6.28319" damping="2" frictionloss="1" />
<geom name="j8" material="robot0:gripper_mat" type="mesh" rgba="0.753 0.753 0.753 1" mesh="link7" />
<geom name="j9" material="robot0:gripper_mat" type="mesh" rgba="1 1 1 1" mesh="base_link" />
<site name="grasp" pos="0 0 0.16" rgba="1 0 0 0" type="sphere" size="0.01" group="1"/>
<body name="left_outer_knuckle" pos="0 0.035 0.059098">
<inertial pos="0 0.021559 0.015181" quat="0.47789 0.87842 0 0" mass="0.033618" diaginertia="1.9111e-05 1.79089e-05 1.90167e-06" />
<joint name="drive_joint" pos="0 0 0" axis="1 0 0" limited="true" range="0 0.85" />
<geom type="mesh" rgba="0 0 0 1" conaffinity="1" contype="0" mesh="left_outer_knuckle" />
<body name="left_finger" pos="0 0.035465 0.042039">
<inertial pos="0 -0.016413 0.029258" quat="0.697634 0.115353 -0.115353 0.697634" mass="0.048304" diaginertia="1.88037e-05 1.7493e-05 3.56792e-06" />
<joint name="left_finger_joint" pos="0 0 0" axis="-1 0 0" limited="true" range="0 0.85" />
<geom name="j10" material="robot0:gripper_finger_mat" type="mesh" rgba="0 0 0 1" conaffinity="3" contype="2" mesh="left_finger" friction='1.5 1.5 1.5' solref='0.01 1' solimp='0.99 0.99 0.01'/>
<body name="right_hand" pos="0 -0.03 0.05" quat="-0.7071 0 0 0.7071">
<site name="ee" pos="0 0 0" rgba="0 0 1 0" type="sphere" group="1"/>
<site name="ee_x" pos="0 0 0" size="0.005 .1" quat="0.707105 0.707108 0 0 " rgba="1 0 0 0" type="cylinder" group="1"/>
<site name="ee_z" pos="0 0 0" size="0.005 .1" quat="0.707105 0 0 0.707108" rgba="0 0 1 0" type="cylinder" group="1"/>
<site name="ee_y" pos="0 0 0" size="0.005 .1" quat="0.707105 0 0.707108 0 " rgba="0 1 0 0" type="cylinder" group="1"/>
</body>
</body>
</body>
<body name="left_inner_knuckle" pos="0 0.02 0.074098">
<inertial pos="1.86601e-06 0.0220468 0.0261335" quat="0.664139 -0.242732 0.242713 0.664146" mass="0.0230126" diaginertia="8.34216e-06 6.0949e-06 2.75601e-06" />
<joint name="left_inner_knuckle_joint" pos="0 0 0" axis="1 0 0" limited="true" range="0 0.85" />
<geom type="mesh" rgba="0 0 0 1" conaffinity="1" contype="0" mesh="left_inner_knuckle" friction='1.5 1.5 1.5' solref='0.01 1' solimp='0.99 0.99 0.01'/>
</body>
<body name="right_outer_knuckle" pos="0 -0.035 0.059098">
<inertial pos="0 -0.021559 0.015181" quat="0.87842 0.47789 0 0" mass="0.033618" diaginertia="1.9111e-05 1.79089e-05 1.90167e-06" />
<joint name="right_outer_knuckle_joint" pos="0 0 0" axis="-1 0 0" limited="true" range="0 0.85" />
<geom type="mesh" rgba="0 0 0 1" conaffinity="1" contype="0" mesh="right_outer_knuckle" />
<body name="right_finger" pos="0 -0.035465 0.042039">
<inertial pos="0 0.016413 0.029258" quat="0.697634 -0.115356 0.115356 0.697634" mass="0.048304" diaginertia="1.88038e-05 1.7493e-05 3.56779e-06" />
<joint name="right_finger_joint" pos="0 0 0" axis="1 0 0" limited="true" range="0 0.85" />
<geom name="j11" material="robot0:gripper_finger_mat" type="mesh" rgba="0 0 0 1" conaffinity="3" contype="2" mesh="right_finger" friction='1.5 1.5 1.5' solref='0.01 1' solimp='0.99 0.99 0.01'/>
<body name="left_hand" pos="0 0.03 0.05" quat="-0.7071 0 0 0.7071">
<site name="ee_2" pos="0 0 0" rgba="1 0 0 0" type="sphere" size="0.01" group="1"/>
</body>
</body>
</body>
<body name="right_inner_knuckle" pos="0 -0.02 0.074098">
<inertial pos="1.866e-06 -0.022047 0.026133" quat="0.66415 0.242702 -0.242721 0.664144" mass="0.023013" diaginertia="8.34209e-06 6.0949e-06 2.75601e-06" />
<joint name="right_inner_knuckle_joint" pos="0 0 0" axis="-1 0 0" limited="true" range="0 0.85" />
<geom type="mesh" rgba="0 0 0 1" conaffinity="1" contype="0" mesh="right_inner_knuckle" friction='1.5 1.5 1.5' solref='0.01 1' solimp='0.99 0.99 0.01'/>
</body>
</body>
</body>
</body>
</body>
</body>
</body>
</body>
</body>
</mujoco>

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envs/sim_xarm/xarm/tasks/base.py Normal file
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import os
import mujoco
import numpy as np
from gymnasium_robotics.envs import robot_env
from xarm.tasks import mocap
class Base(robot_env.MujocoRobotEnv):
"""
Superclass for all simxarm environments.
Args:
xml_name (str): name of the xml environment file
gripper_rotation (list): initial rotation of the gripper (given as a quaternion)
"""
def __init__(self, xml_name, gripper_rotation=None):
if gripper_rotation is None:
gripper_rotation = [0, 1, 0, 0]
self.gripper_rotation = np.array(gripper_rotation, dtype=np.float32)
self.center_of_table = np.array([1.655, 0.3, 0.63625])
self.max_z = 1.2
self.min_z = 0.2
super().__init__(
model_path=os.path.join(os.path.dirname(__file__), "assets", xml_name + ".xml"),
n_substeps=20,
n_actions=4,
initial_qpos={},
)
@property
def dt(self):
return self.n_substeps * self.model.opt.timestep
@property
def eef(self):
return self._utils.get_site_xpos(self.model, self.data, "grasp")
@property
def obj(self):
return self._utils.get_site_xpos(self.model, self.data, "object_site")
@property
def robot_state(self):
gripper_angle = self._utils.get_joint_qpos(self.model, self.data, "right_outer_knuckle_joint")
return np.concatenate([self.eef, gripper_angle])
def is_success(self):
return NotImplementedError()
def get_reward(self):
raise NotImplementedError()
def _sample_goal(self):
raise NotImplementedError()
def get_obs(self):
return self._get_obs()
def _step_callback(self):
self._mujoco.mj_forward(self.model, self.data)
def _limit_gripper(self, gripper_pos, pos_ctrl):
if gripper_pos[0] > self.center_of_table[0] - 0.105 + 0.15:
pos_ctrl[0] = min(pos_ctrl[0], 0)
if gripper_pos[0] < self.center_of_table[0] - 0.105 - 0.3:
pos_ctrl[0] = max(pos_ctrl[0], 0)
if gripper_pos[1] > self.center_of_table[1] + 0.3:
pos_ctrl[1] = min(pos_ctrl[1], 0)
if gripper_pos[1] < self.center_of_table[1] - 0.3:
pos_ctrl[1] = max(pos_ctrl[1], 0)
if gripper_pos[2] > self.max_z:
pos_ctrl[2] = min(pos_ctrl[2], 0)
if gripper_pos[2] < self.min_z:
pos_ctrl[2] = max(pos_ctrl[2], 0)
return pos_ctrl
def _apply_action(self, action):
assert action.shape == (4,)
action = action.copy()
pos_ctrl, gripper_ctrl = action[:3], action[3]
pos_ctrl = self._limit_gripper(
self._utils.get_site_xpos(self.model, self.data, "grasp"), pos_ctrl
) * (1 / self.n_substeps)
gripper_ctrl = np.array([gripper_ctrl, gripper_ctrl])
mocap.apply_action(
self.model,
self._model_names,
self.data,
np.concatenate([pos_ctrl, self.gripper_rotation, gripper_ctrl]),
)
def _render_callback(self):
self._mujoco.mj_forward(self.model, self.data)
def _reset_sim(self):
self.data.time = self.initial_time
self.data.qpos[:] = np.copy(self.initial_qpos)
self.data.qvel[:] = np.copy(self.initial_qvel)
self._sample_goal()
self._mujoco.mj_step(self.model, self.data, nstep=10)
return True
def _set_gripper(self, gripper_pos, gripper_rotation):
self._utils.set_mocap_pos(self.model, self.data, "robot0:mocap", gripper_pos)
self._utils.set_mocap_quat(self.model, self.data, "robot0:mocap", gripper_rotation)
self._utils.set_joint_qpos(self.model, self.data, "right_outer_knuckle_joint", 0)
self.data.qpos[10] = 0.0
self.data.qpos[12] = 0.0
def _env_setup(self, initial_qpos):
for name, value in initial_qpos.items():
self.data.set_joint_qpos(name, value)
mocap.reset(self.model, self.data)
mujoco.mj_forward(self.model, self.data)
self._sample_goal()
mujoco.mj_forward(self.model, self.data)
def reset(self):
self._reset_sim()
return self._get_obs()
def step(self, action):
assert action.shape == (4,)
assert self.action_space.contains(action), "{!r} ({}) invalid".format(action, type(action))
self._apply_action(action)
self._mujoco.mj_step(self.model, self.data, nstep=2)
self._step_callback()
obs = self._get_obs()
reward = self.get_reward()
done = False
info = {"is_success": self.is_success(), "success": self.is_success()}
return obs, reward, done, info
def render(self, mode="rgb_array", width=384, height=384):
self._render_callback()
# HACK
self.model.vis.global_.offwidth = width
self.model.vis.global_.offheight = height
return self.mujoco_renderer.render(mode)
def close(self):
if self.mujoco_renderer is not None:
self.mujoco_renderer.close()

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envs/sim_xarm/xarm/tasks/lift.py Normal file
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import numpy as np
from xarm import Base
class Lift(Base):
def __init__(self):
self._z_threshold = 0.15
super().__init__("lift")
@property
def z_target(self):
return self._init_z + self._z_threshold
def is_success(self):
return self.obj[2] >= self.z_target
def get_reward(self):
reach_dist = np.linalg.norm(self.obj - self.eef)
reach_dist_xy = np.linalg.norm(self.obj[:-1] - self.eef[:-1])
pick_completed = self.obj[2] >= (self.z_target - 0.01)
obj_dropped = (self.obj[2] < (self._init_z + 0.005)) and (reach_dist > 0.02)
# Reach
if reach_dist < 0.05:
reach_reward = -reach_dist + max(self._action[-1], 0) / 50
elif reach_dist_xy < 0.05:
reach_reward = -reach_dist
else:
z_bonus = np.linalg.norm(np.linalg.norm(self.obj[-1] - self.eef[-1]))
reach_reward = -reach_dist - 2 * z_bonus
# Pick
if pick_completed and not obj_dropped:
pick_reward = self.z_target
elif (reach_dist < 0.1) and (self.obj[2] > (self._init_z + 0.005)):
pick_reward = min(self.z_target, self.obj[2])
else:
pick_reward = 0
return reach_reward / 100 + pick_reward
def _get_obs(self):
eef_velp = self._utils.get_site_xvelp(self.model, self.data, "grasp") * self.dt
gripper_angle = self._utils.get_joint_qpos(self.model, self.data, "right_outer_knuckle_joint")
eef = self.eef - self.center_of_table
obj = self.obj - self.center_of_table
obj_rot = self._utils.get_joint_qpos(self.model, self.data, "object_joint0")[-4:]
obj_velp = self._utils.get_site_xvelp(self.model, self.data, "object_site") * self.dt
obj_velr = self._utils.get_site_xvelr(self.model, self.data, "object_site") * self.dt
obs = np.concatenate(
[
eef,
eef_velp,
obj,
obj_rot,
obj_velp,
obj_velr,
eef - obj,
np.array(
[
np.linalg.norm(eef - obj),
np.linalg.norm(eef[:-1] - obj[:-1]),
self.z_target,
self.z_target - obj[-1],
self.z_target - eef[-1],
]
),
gripper_angle,
],
axis=0,
)
return {"observation": obs, "state": eef, "achieved_goal": eef, "desired_goal": eef}
def _sample_goal(self):
# Gripper
gripper_pos = np.array([1.280, 0.295, 0.735]) + self.np_random.uniform(-0.05, 0.05, size=3)
super()._set_gripper(gripper_pos, self.gripper_rotation)
# Object
object_pos = self.center_of_table - np.array([0.15, 0.10, 0.07])
object_pos[0] += self.np_random.uniform(-0.05, 0.05, size=1)
object_pos[1] += self.np_random.uniform(-0.05, 0.05, size=1)
object_qpos = self._utils.get_joint_qpos(self.model, self.data, "object_joint0")
object_qpos[:3] = object_pos
self._utils.set_joint_qpos(self.model, self.data, "object_joint0", object_qpos)
self._init_z = object_pos[2]
# Goal
return object_pos + np.array([0, 0, self._z_threshold])
def reset(self):
self._action = np.zeros(4)
return super().reset()
def step(self, action):
self._action = action.copy()
return super().step(action)

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envs/sim_xarm/xarm/tasks/mocap.py Normal file
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# import mujoco_py
import mujoco
import numpy as np
def apply_action(model, model_names, data, action):
if model.nmocap > 0:
pos_action, gripper_action = np.split(action, (model.nmocap * 7,))
if data.ctrl is not None:
for i in range(gripper_action.shape[0]):
data.ctrl[i] = gripper_action[i]
pos_action = pos_action.reshape(model.nmocap, 7)
pos_delta, quat_delta = pos_action[:, :3], pos_action[:, 3:]
reset_mocap2body_xpos(model, model_names, data)
data.mocap_pos[:] = data.mocap_pos + pos_delta
data.mocap_quat[:] = data.mocap_quat + quat_delta
def reset(model, data):
if model.nmocap > 0 and model.eq_data is not None:
for i in range(model.eq_data.shape[0]):
# if sim.model.eq_type[i] == mujoco_py.const.EQ_WELD:
if model.eq_type[i] == mujoco.mjtEq.mjEQ_WELD:
# model.eq_data[i, :] = np.array([0., 0., 0., 1., 0., 0., 0.])
model.eq_data[i, :] = np.array(
[
0.0,
0.0,
0.0,
1.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
]
)
# sim.forward()
mujoco.mj_forward(model, data)
def reset_mocap2body_xpos(model, model_names, data):
if model.eq_type is None or model.eq_obj1id is None or model.eq_obj2id is None:
return
# For all weld constraints
for eq_type, obj1_id, obj2_id in zip(model.eq_type, model.eq_obj1id, model.eq_obj2id, strict=False):
# if eq_type != mujoco_py.const.EQ_WELD:
if eq_type != mujoco.mjtEq.mjEQ_WELD:
continue
# body2 = model.body_id2name(obj2_id)
body2 = model_names.body_id2name[obj2_id]
if body2 == "B0" or body2 == "B9" or body2 == "B1":
continue
mocap_id = model.body_mocapid[obj1_id]
if mocap_id != -1:
# obj1 is the mocap, obj2 is the welded body
body_idx = obj2_id
else:
# obj2 is the mocap, obj1 is the welded body
mocap_id = model.body_mocapid[obj2_id]
body_idx = obj1_id
assert mocap_id != -1
data.mocap_pos[mocap_id][:] = data.xpos[body_idx]
data.mocap_quat[mocap_id][:] = data.xquat[body_idx]

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envs/sim_xarm/xarm/tasks/peg_in_box.py Normal file
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import numpy as np
from xarm import Base
class PegInBox(Base):
def __init__(self):
super().__init__("peg_in_box")
def _reset_sim(self):
self._act_magnitude = 0
super()._reset_sim()
for _ in range(10):
self._apply_action(np.array([0, 0, 0, 1], dtype=np.float32))
self.sim.step()
@property
def box(self):
return self.sim.data.get_site_xpos("box_site")
def is_success(self):
return np.linalg.norm(self.obj - self.box) <= 0.05
def get_reward(self):
dist_xy = np.linalg.norm(self.obj[:2] - self.box[:2])
dist_xyz = np.linalg.norm(self.obj - self.box)
return float(dist_xy <= 0.045) * (2 - 6 * dist_xyz) - 0.2 * np.square(self._act_magnitude) - dist_xy
def _get_obs(self):
eef_velp = self.sim.data.get_site_xvelp("grasp") * self.dt
gripper_angle = self.sim.data.get_joint_qpos("right_outer_knuckle_joint")
eef, box = self.eef - self.center_of_table, self.box - self.center_of_table
obj = self.obj - self.center_of_table
obj_rot = self.sim.data.get_joint_qpos("object_joint0")[-4:]
obj_velp = self.sim.data.get_site_xvelp("object_site") * self.dt
obj_velr = self.sim.data.get_site_xvelr("object_site") * self.dt
obs = np.concatenate(
[
eef,
eef_velp,
box,
obj,
obj_rot,
obj_velp,
obj_velr,
eef - box,
eef - obj,
obj - box,
np.array(
[
np.linalg.norm(eef - box),
np.linalg.norm(eef - obj),
np.linalg.norm(obj - box),
gripper_angle,
]
),
],
axis=0,
)
return {"observation": obs, "state": eef, "achieved_goal": eef, "desired_goal": box}
def _sample_goal(self):
# Gripper
gripper_pos = np.array([1.280, 0.295, 0.9]) + self.np_random.uniform(-0.05, 0.05, size=3)
super()._set_gripper(gripper_pos, self.gripper_rotation)
# Object
object_pos = gripper_pos - np.array([0, 0, 0.06]) + self.np_random.uniform(-0.005, 0.005, size=3)
object_qpos = self.sim.data.get_joint_qpos("object_joint0")
object_qpos[:3] = object_pos
self.sim.data.set_joint_qpos("object_joint0", object_qpos)
# Box
box_pos = np.array([1.61, 0.18, 0.58])
box_pos[:2] += self.np_random.uniform(-0.11, 0.11, size=2)
box_qpos = self.sim.data.get_joint_qpos("box_joint0")
box_qpos[:3] = box_pos
self.sim.data.set_joint_qpos("box_joint0", box_qpos)
return self.box
def step(self, action):
self._act_magnitude = np.linalg.norm(action[:3])
return super().step(action)

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envs/sim_xarm/xarm/tasks/push.py Normal file
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import numpy as np
from xarm import Base
class Push(Base):
def __init__(self):
super().__init__("push")
def _reset_sim(self):
self._act_magnitude = 0
super()._reset_sim()
def is_success(self):
return np.linalg.norm(self.obj - self.goal) <= 0.05
def get_reward(self):
dist = np.linalg.norm(self.obj - self.goal)
penalty = self._act_magnitude**2
return -(dist + 0.15 * penalty)
def _get_obs(self):
eef_velp = self.sim.data.get_site_xvelp("grasp") * self.dt
gripper_angle = self.sim.data.get_joint_qpos("right_outer_knuckle_joint")
eef, goal = self.eef - self.center_of_table, self.goal - self.center_of_table
obj = self.obj - self.center_of_table
obj_rot = self.sim.data.get_joint_qpos("object_joint0")[-4:]
obj_velp = self.sim.data.get_site_xvelp("object_site") * self.dt
obj_velr = self.sim.data.get_site_xvelr("object_site") * self.dt
obs = np.concatenate(
[
eef,
eef_velp,
goal,
obj,
obj_rot,
obj_velp,
obj_velr,
eef - goal,
eef - obj,
obj - goal,
np.array(
[
np.linalg.norm(eef - goal),
np.linalg.norm(eef - obj),
np.linalg.norm(obj - goal),
gripper_angle,
]
),
],
axis=0,
)
return {"observation": obs, "state": eef, "achieved_goal": eef, "desired_goal": goal}
def _sample_goal(self):
# Gripper
gripper_pos = np.array([1.280, 0.295, 0.735]) + self.np_random.uniform(-0.05, 0.05, size=3)
super()._set_gripper(gripper_pos, self.gripper_rotation)
# Object
object_pos = self.center_of_table - np.array([0.25, 0, 0.07])
object_pos[0] += self.np_random.uniform(-0.08, 0.08, size=1)
object_pos[1] += self.np_random.uniform(-0.08, 0.08, size=1)
object_qpos = self.sim.data.get_joint_qpos("object_joint0")
object_qpos[:3] = object_pos
self.sim.data.set_joint_qpos("object_joint0", object_qpos)
# Goal
self.goal = np.array([1.600, 0.200, 0.545])
self.goal[:2] += self.np_random.uniform(-0.1, 0.1, size=2)
self.sim.model.site_pos[self.sim.model.site_name2id("target0")] = self.goal
return self.goal
def step(self, action):
self._act_magnitude = np.linalg.norm(action[:3])
return super().step(action)

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import numpy as np
from xarm import Base
class Reach(Base):
def __init__(self):
super().__init__("reach")
def _reset_sim(self):
self._act_magnitude = 0
super()._reset_sim()
def is_success(self):
return np.linalg.norm(self.eef - self.goal) <= 0.05
def get_reward(self):
dist = np.linalg.norm(self.eef - self.goal)
penalty = self._act_magnitude**2
return -(dist + 0.15 * penalty)
def _get_obs(self):
eef_velp = self.sim.data.get_site_xvelp("grasp") * self.dt
gripper_angle = self.sim.data.get_joint_qpos("right_outer_knuckle_joint")
eef, goal = self.eef - self.center_of_table, self.goal - self.center_of_table
obs = np.concatenate(
[eef, eef_velp, goal, eef - goal, np.array([np.linalg.norm(eef - goal), gripper_angle])], axis=0
)
return {"observation": obs, "state": eef, "achieved_goal": eef, "desired_goal": goal}
def _sample_goal(self):
# Gripper
gripper_pos = np.array([1.280, 0.295, 0.735]) + self.np_random.uniform(-0.05, 0.05, size=3)
super()._set_gripper(gripper_pos, self.gripper_rotation)
# Goal
self.goal = np.array([1.550, 0.287, 0.580])
self.goal[:2] += self.np_random.uniform(-0.125, 0.125, size=2)
self.sim.model.site_pos[self.sim.model.site_name2id("target0")] = self.goal
return self.goal
def step(self, action):
self._act_magnitude = np.linalg.norm(action[:3])
return super().step(action)