WIP Upgrading simxam from mujoco-py to mujoco python bindings
This commit is contained in:
Simon Alibert
parent
e41c420a96
commit
1c24bbda3f
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@ -1 +1,2 @@
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*.memmap filter=lfs diff=lfs merge=lfs -text
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*.stl filter=lfs diff=lfs merge=lfs -text
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@ -32,6 +32,7 @@ class AbstractExperienceReplay(TensorDictReplayBuffer):
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collate_fn: Callable = None,
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writer: Writer = None,
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transform: "torchrl.envs.Transform" = None,
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# storage = None,
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):
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self.dataset_id = dataset_id
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self.version = version
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@ -43,7 +44,12 @@ class AbstractExperienceReplay(TensorDictReplayBuffer):
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f"The version of the dataset ({self.version}) is not enforced when root is provided ({self.root})."
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)
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storage = self._download_or_load_dataset()
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# HACK
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if dataset_id == "xarm_lift_medium":
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self.data_dir = self.root / self.dataset_id
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storage = self._download_and_preproc_obsolete()
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else:
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storage = self._download_or_load_dataset()
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super().__init__(
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storage=storage,
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@ -67,11 +67,11 @@ class SimxarmExperienceReplay(AbstractExperienceReplay):
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)
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def _download_and_preproc_obsolete(self):
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assert self.root is not None
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# assert self.root is not None
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# TODO(rcadene): finish download
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download()
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# download()
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dataset_path = self.root / f"{self.dataset_id}_raw" / "buffer.pkl"
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dataset_path = self.root / f"{self.dataset_id}" / "buffer.pkl"
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print(f"Using offline dataset '{dataset_path}'")
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with open(dataset_path, "rb") as f:
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dataset_dict = pickle.load(f)
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@ -105,8 +105,8 @@ class SimxarmExperienceReplay(AbstractExperienceReplay):
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"frame_id": torch.arange(0, num_frames, 1),
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("next", "observation", "image"): next_image,
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("next", "observation", "state"): next_state,
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("next", "observation", "reward"): next_reward,
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("next", "observation", "done"): next_done,
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("next", "reward"): next_reward,
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("next", "done"): next_done,
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},
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batch_size=num_frames,
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)
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@ -17,7 +17,7 @@ def make_env(cfg, transform=None):
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}
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if cfg.env.name == "simxarm":
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from lerobot.common.envs.simxarm import SimxarmEnv
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from lerobot.common.envs.simxarm.env import SimxarmEnv
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kwargs["task"] = cfg.env.task
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clsfunc = SimxarmEnv
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@ -19,8 +19,8 @@ from lerobot.common.utils import set_seed
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MAX_NUM_ACTIONS = 4
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_has_gym = importlib.util.find_spec("gym") is not None
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_has_simxarm = importlib.util.find_spec("simxarm") is not None and _has_gym
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_has_gym = importlib.util.find_spec("gymnasium") is not None
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# _has_simxarm = importlib.util.find_spec("simxarm") is not None and _has_gym
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class SimxarmEnv(AbstractEnv):
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@ -49,13 +49,14 @@ class SimxarmEnv(AbstractEnv):
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)
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def _make_env(self):
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if not _has_simxarm:
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raise ImportError("Cannot import simxarm.")
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# if not _has_simxarm:
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# raise ImportError("Cannot import simxarm.")
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if not _has_gym:
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raise ImportError("Cannot import gym.")
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import gym
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from simxarm import TASKS
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import gymnasium
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from lerobot.common.envs.simxarm.simxarm import TASKS
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if self.task not in TASKS:
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raise ValueError(f"Unknown task {self.task}. Must be one of {list(TASKS.keys())}")
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@ -63,7 +64,7 @@ class SimxarmEnv(AbstractEnv):
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self._env = TASKS[self.task]["env"]()
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num_actions = len(TASKS[self.task]["action_space"])
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self._action_space = gym.spaces.Box(low=-1.0, high=1.0, shape=(num_actions,))
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self._action_space = gymnasium.spaces.Box(low=-1.0, high=1.0, shape=(num_actions,))
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self._action_padding = np.zeros((MAX_NUM_ACTIONS - num_actions), dtype=np.float32)
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if "w" not in TASKS[self.task]["action_space"]:
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self._action_padding[-1] = 1.0
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@ -230,4 +231,7 @@ class SimxarmEnv(AbstractEnv):
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def _set_seed(self, seed: Optional[int]):
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set_seed(seed)
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self._env.seed(seed)
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# self._env.seed(seed)
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# self._env.action_space.seed(seed)
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# self.set_seed(seed)
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self._seed = seed
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@ -0,0 +1,165 @@
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from collections import OrderedDict, deque
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import gym
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import numpy as np
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from gym.wrappers import TimeLimit
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from lerobot.common.envs.simxarm.simxarm.task.lift import Lift
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from lerobot.common.envs.simxarm.simxarm.task.peg_in_box import PegInBox
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from lerobot.common.envs.simxarm.simxarm.task.push import Push
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from lerobot.common.envs.simxarm.simxarm.task.reach import Reach
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TASKS = OrderedDict(
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(
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(
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"reach",
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{
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"env": Reach,
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"action_space": "xyz",
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"episode_length": 50,
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"description": "Reach a target location with the end effector",
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},
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),
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(
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"push",
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{
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"env": Push,
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"action_space": "xyz",
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"episode_length": 50,
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"description": "Push a cube to a target location",
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},
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),
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(
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"peg_in_box",
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{
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"env": PegInBox,
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"action_space": "xyz",
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"episode_length": 50,
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"description": "Insert a peg into a box",
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},
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),
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(
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"lift",
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{
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"env": Lift,
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"action_space": "xyzw",
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"episode_length": 50,
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"description": "Lift a cube above a height threshold",
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},
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),
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)
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)
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class SimXarmWrapper(gym.Wrapper):
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"""
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A wrapper for the SimXarm environments. This wrapper is used to
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convert the action and observation spaces to the correct format.
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"""
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def __init__(self, env, task, obs_mode, image_size, action_repeat, frame_stack=1, channel_last=False):
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super().__init__(env)
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self._env = env
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self.obs_mode = obs_mode
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self.image_size = image_size
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self.action_repeat = action_repeat
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self.frame_stack = frame_stack
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self._frames = deque([], maxlen=frame_stack)
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self.channel_last = channel_last
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self._max_episode_steps = task["episode_length"] // action_repeat
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image_shape = (
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(image_size, image_size, 3 * frame_stack)
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if channel_last
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else (3 * frame_stack, image_size, image_size)
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)
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if obs_mode == "state":
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self.observation_space = env.observation_space["observation"]
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elif obs_mode == "rgb":
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self.observation_space = gym.spaces.Box(low=0, high=255, shape=image_shape, dtype=np.uint8)
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elif obs_mode == "all":
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self.observation_space = gym.spaces.Dict(
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state=gym.spaces.Box(low=-np.inf, high=np.inf, shape=(4,), dtype=np.float32),
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rgb=gym.spaces.Box(low=0, high=255, shape=image_shape, dtype=np.uint8),
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)
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else:
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raise ValueError(f"Unknown obs_mode {obs_mode}. Must be one of [rgb, all, state]")
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self.action_space = gym.spaces.Box(low=-1.0, high=1.0, shape=(len(task["action_space"]),))
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self.action_padding = np.zeros(4 - len(task["action_space"]), dtype=np.float32)
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if "w" not in task["action_space"]:
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self.action_padding[-1] = 1.0
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def _render_obs(self):
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obs = self.render(mode="rgb_array", width=self.image_size, height=self.image_size)
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if not self.channel_last:
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obs = obs.transpose(2, 0, 1)
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return obs.copy()
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def _update_frames(self, reset=False):
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pixels = self._render_obs()
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self._frames.append(pixels)
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if reset:
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for _ in range(1, self.frame_stack):
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self._frames.append(pixels)
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assert len(self._frames) == self.frame_stack
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def transform_obs(self, obs, reset=False):
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if self.obs_mode == "state":
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return obs["observation"]
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elif self.obs_mode == "rgb":
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self._update_frames(reset=reset)
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rgb_obs = np.concatenate(list(self._frames), axis=-1 if self.channel_last else 0)
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return rgb_obs
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elif self.obs_mode == "all":
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self._update_frames(reset=reset)
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rgb_obs = np.concatenate(list(self._frames), axis=-1 if self.channel_last else 0)
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return OrderedDict((("rgb", rgb_obs), ("state", self.robot_state)))
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else:
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raise ValueError(f"Unknown obs_mode {self.obs_mode}. Must be one of [rgb, all, state]")
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def reset(self):
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return self.transform_obs(self._env.reset(), reset=True)
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def step(self, action):
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action = np.concatenate([action, self.action_padding])
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reward = 0.0
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for _ in range(self.action_repeat):
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obs, r, done, info = self._env.step(action)
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reward += r
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return self.transform_obs(obs), reward, done, info
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def render(self, mode="rgb_array", width=384, height=384, **kwargs):
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return self._env.render(mode, width=width, height=height)
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@property
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def state(self):
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return self._env.robot_state
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def make(task, obs_mode="state", image_size=84, action_repeat=1, frame_stack=1, channel_last=False, seed=0):
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"""
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Create a new environment.
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Args:
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task (str): The task to create an environment for. Must be one of:
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- 'reach'
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- 'push'
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- 'peg-in-box'
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- 'lift'
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obs_mode (str): The observation mode to use. Must be one of:
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- 'state': Only state observations
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- 'rgb': RGB images
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- 'all': RGB images and state observations
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image_size (int): The size of the image observations
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action_repeat (int): The number of times to repeat the action
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seed (int): The random seed to use
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Returns:
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gym.Env: The environment
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"""
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if task not in TASKS:
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raise ValueError(f"Unknown task {task}. Must be one of {list(TASKS.keys())}")
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env = TASKS[task]["env"]()
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env = TimeLimit(env, TASKS[task]["episode_length"])
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env = SimXarmWrapper(env, TASKS[task], obs_mode, image_size, action_repeat, frame_stack, channel_last)
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env.seed(seed)
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return env
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@ -0,0 +1,53 @@
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<?xml version="1.0" encoding="utf-8"?>
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<mujoco>
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<compiler angle="radian" coordinate="local" meshdir="mesh" texturedir="texture"></compiler>
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<size nconmax="2000" njmax="500"/>
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<option timestep="0.002">
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<flag warmstart="enable"></flag>
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</option>
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<include file="shared.xml"></include>
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<worldbody>
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<body name="floor0" pos="0 0 0">
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<geom name="floorgeom0" pos="1.2 -2.0 0" size="20.0 20.0 1" type="plane" condim="3" material="floor_mat"></geom>
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</body>
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<include file="xarm.xml"></include>
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<body pos="0.75 0 0.6325" name="pedestal0">
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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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<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" />
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</body>
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<body pos="1.5 0.075 0.3425" name="table0">
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<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>
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</body>
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<body name="object" pos="1.405 0.3 0.58625">
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<joint name="object_joint0" type="free" limited="false"></joint>
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<geom size="0.035 0.035 0.035" type="box" name="object0" material="block_mat" density="50000" condim="4" friction="1 1 1" solimp="1 1 1" solref="0.02 1"></geom>
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<site name="object_site" pos="0 0 0" size="0.035 0.035 0.035" rgba="1 0 0 0" type="box"></site>
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</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="1.65 0 10" dir="-0.57 -0.57 -0.57" name="light0"></light>
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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 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>
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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>
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<camera fovy="50" name="camera0" pos="0.9559 1.0 1.1" euler="-1.1 -0.6 3.4" />
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</worldbody>
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<equality>
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<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>
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<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>
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<joint joint1="left_inner_knuckle_joint" joint2="right_inner_knuckle_joint"></joint>
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</equality>
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<actuator>
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<motor ctrllimited="true" ctrlrange="-1.0 1.0" joint="left_inner_knuckle_joint" gear="200.0"/>
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<motor ctrllimited="true" ctrlrange="-1.0 1.0" joint="right_inner_knuckle_joint" gear="200.0"/>
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</actuator>
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</mujoco>
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version https://git-lfs.github.com/spec/v1
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<?xml version="1.0" encoding="utf-8"?>
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||||
|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
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|
||||
|
||||
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|
||||
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|
||||
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|
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|
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|
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|
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|
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|
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|
||||
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|
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|
||||
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|
||||
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|
||||
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||||
|
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|
||||
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||||
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|
||||
|
||||
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|
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|
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|
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|
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|
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|
||||
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|
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|
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|
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|
||||
|
|
@ -0,0 +1,88 @@
|
|||
<mujoco model="xarm7">
|
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|
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|
||||
<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>
|
||||
|
|
@ -0,0 +1,170 @@
|
|||
import os
|
||||
|
||||
import glfw
|
||||
import mujoco
|
||||
import numpy as np
|
||||
|
||||
# import gym
|
||||
# from gym.envs.robotics import robot_env
|
||||
from gymnasium_robotics.envs import robot_env
|
||||
|
||||
from lerobot.common.envs.simxarm.simxarm.task 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.sim.forward()
|
||||
|
||||
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.sim, np.concatenate([pos_ctrl, self.gripper_rotation, gripper_ctrl]))
|
||||
|
||||
def _viewer_setup(self):
|
||||
body_id = self.sim.model.body_name2id("link7")
|
||||
lookat = self.sim.data.body_xpos[body_id]
|
||||
for idx, value in enumerate(lookat):
|
||||
self.viewer.cam.lookat[idx] = value
|
||||
self.viewer.cam.distance = 4.0
|
||||
self.viewer.cam.azimuth = 132.0
|
||||
self.viewer.cam.elevation = -14.0
|
||||
|
||||
def _render_callback(self):
|
||||
# self.sim.forward()
|
||||
self._mujoco.mj_forward(self.model, self.data)
|
||||
|
||||
def _reset_sim(self):
|
||||
# self.sim.set_state(self.initial_state)
|
||||
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()
|
||||
for _ in range(10):
|
||||
# self.sim.step()
|
||||
self._mujoco.mj_forward(self.model, self.data)
|
||||
return True
|
||||
|
||||
def _set_gripper(self, gripper_pos, gripper_rotation):
|
||||
# self.data.set_mocap_pos('robot0:mocap2', gripper_pos)
|
||||
# self.data.set_mocap_quat('robot0:mocap2', gripper_rotation)
|
||||
# self.data.set_joint_qpos('right_outer_knuckle_joint', 0)
|
||||
self._utils.set_mocap_pos(self.model, self.data, "robot0:mocap", gripper_pos)
|
||||
# self._utils.set_mocap_pos(self.model, self.data, "robot0:mocap", gripper_rotation)
|
||||
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.sim.data.set_joint_qpos(name, value)
|
||||
self.data.set_joint_qpos(name, value)
|
||||
mocap.reset(self.model, self.data)
|
||||
mujoco.mj_forward(self.model, self.data)
|
||||
# self.sim.forward()
|
||||
self._sample_goal()
|
||||
# self.sim.forward()
|
||||
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)
|
||||
for _ in range(2):
|
||||
self.sim.step()
|
||||
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()
|
||||
# if mode == 'rgb_array':
|
||||
# return self.sim.render(width, height, camera_name='camera0', depth=False)[::-1, :, :]
|
||||
# elif mode == "human":
|
||||
# self._get_viewer(mode).render()
|
||||
return self.mujoco_renderer.render(mode)
|
||||
|
||||
def close(self):
|
||||
if self.viewer is not None:
|
||||
# self.viewer.finish()
|
||||
print("Closing window glfw")
|
||||
glfw.destroy_window(self.viewer.window)
|
||||
self.viewer = None
|
||||
self._viewers = {}
|
||||
|
|
@ -0,0 +1,101 @@
|
|||
import numpy as np
|
||||
|
||||
from lerobot.common.envs.simxarm.simxarm 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.sim.data.set_joint_qpos('object_joint0', object_qpos)
|
||||
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)
|
||||
|
|
@ -0,0 +1,68 @@
|
|||
# import mujoco_py
|
||||
import mujoco
|
||||
import numpy as np
|
||||
|
||||
|
||||
def apply_action(sim, action):
|
||||
if sim.model.nmocap > 0:
|
||||
pos_action, gripper_action = np.split(action, (sim.model.nmocap * 7,))
|
||||
if sim.data.ctrl is not None:
|
||||
for i in range(gripper_action.shape[0]):
|
||||
sim.data.ctrl[i] = gripper_action[i]
|
||||
pos_action = pos_action.reshape(sim.model.nmocap, 7)
|
||||
pos_delta, quat_delta = pos_action[:, :3], pos_action[:, 3:]
|
||||
reset_mocap2body_xpos(sim)
|
||||
sim.data.mocap_pos[:] = sim.data.mocap_pos + pos_delta
|
||||
sim.data.mocap_quat[:] = sim.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(sim):
|
||||
if sim.model.eq_type is None or sim.model.eq_obj1id is None or sim.model.eq_obj2id is None:
|
||||
return
|
||||
|
||||
# For all weld constraints
|
||||
for eq_type, obj1_id, obj2_id in zip(
|
||||
sim.model.eq_type, sim.model.eq_obj1id, sim.model.eq_obj2id, strict=False
|
||||
):
|
||||
# if eq_type != mujoco_py.const.EQ_WELD:
|
||||
if eq_type != mujoco.mjtEq.mjEQ_WELD:
|
||||
continue
|
||||
body2 = sim.model.body_id2name(obj2_id)
|
||||
if body2 == "B0" or body2 == "B9" or body2 == "B1":
|
||||
continue
|
||||
mocap_id = sim.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 = sim.model.body_mocapid[obj2_id]
|
||||
body_idx = obj1_id
|
||||
assert mocap_id != -1
|
||||
sim.data.mocap_pos[mocap_id][:] = sim.data.body_xpos[body_idx]
|
||||
sim.data.mocap_quat[mocap_id][:] = sim.data.body_xquat[body_idx]
|
||||
|
|
@ -0,0 +1,86 @@
|
|||
import numpy as np
|
||||
|
||||
from lerobot.common.envs.simxarm.simxarm 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)
|
||||
|
|
@ -0,0 +1,78 @@
|
|||
import numpy as np
|
||||
|
||||
from lerobot.common.envs.simxarm.simxarm 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)
|
||||
|
|
@ -0,0 +1,44 @@
|
|||
import numpy as np
|
||||
|
||||
from lerobot.common.envs.simxarm.simxarm 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)
|
||||
|
|
@ -1,6 +1,7 @@
|
|||
# @package _global_
|
||||
|
||||
n_action_steps: 1
|
||||
n_obs_steps: 1
|
||||
|
||||
policy:
|
||||
name: tdmpc
|
||||
|
|
|
|||
|
|
@ -338,73 +338,6 @@ files = [
|
|||
{file = "colorama-0.4.6.tar.gz", hash = "sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "cython"
|
||||
version = "3.0.9"
|
||||
description = "The Cython compiler for writing C extensions in the Python language."
|
||||
optional = false
|
||||
python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
|
||||
files = [
|
||||
{file = "Cython-3.0.9-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:296bd30d4445ac61b66c9d766567f6e81a6e262835d261e903c60c891a6729d3"},
|
||||
{file = "Cython-3.0.9-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f496b52845cb45568a69d6359a2c335135233003e708ea02155c10ce3548aa89"},
|
||||
{file = "Cython-3.0.9-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:858c3766b9aa3ab8a413392c72bbab1c144a9766b7c7bfdef64e2e414363fa0c"},
|
||||
{file = "Cython-3.0.9-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:c0eb1e6ef036028a52525fd9a012a556f6dd4788a0e8755fe864ba0e70cde2ff"},
|
||||
{file = "Cython-3.0.9-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:c8191941073ea5896321de3c8c958fd66e5f304b0cd1f22c59edd0b86c4dd90d"},
|
||||
{file = "Cython-3.0.9-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:e32b016030bc72a8a22a1f21f470a2f57573761a4f00fbfe8347263f4fbdb9f1"},
|
||||
{file = "Cython-3.0.9-cp310-cp310-win32.whl", hash = "sha256:d6f3ff1cd6123973fe03e0fb8ee936622f976c0c41138969975824d08886572b"},
|
||||
{file = "Cython-3.0.9-cp310-cp310-win_amd64.whl", hash = "sha256:56f3b643dbe14449248bbeb9a63fe3878a24256664bc8c8ef6efd45d102596d8"},
|
||||
{file = "Cython-3.0.9-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:35e6665a20d6b8a152d72b7fd87dbb2af6bb6b18a235b71add68122d594dbd41"},
|
||||
{file = "Cython-3.0.9-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f92f4960c40ad027bd8c364c50db11104eadc59ffeb9e5b7f605ca2f05946e20"},
|
||||
{file = "Cython-3.0.9-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:38df37d0e732fbd9a2fef898788492e82b770c33d1e4ed12444bbc8a3b3f89c0"},
|
||||
{file = "Cython-3.0.9-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:ad7fd88ebaeaf2e76fd729a8919fae80dab3d6ac0005e28494261d52ff347a8f"},
|
||||
{file = "Cython-3.0.9-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:1365d5f76bf4d19df3d19ce932584c9bb76e9fb096185168918ef9b36e06bfa4"},
|
||||
{file = "Cython-3.0.9-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:c232e7f279388ac9625c3e5a5a9f0078a9334959c5d6458052c65bbbba895e1e"},
|
||||
{file = "Cython-3.0.9-cp311-cp311-win32.whl", hash = "sha256:357e2fad46a25030b0c0496487e01a9dc0fdd0c09df0897f554d8ba3c1bc4872"},
|
||||
{file = "Cython-3.0.9-cp311-cp311-win_amd64.whl", hash = "sha256:1315aee506506e8d69cf6631d8769e6b10131fdcc0eb66df2698f2a3ddaeeff2"},
|
||||
{file = "Cython-3.0.9-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:157973807c2796addbed5fbc4d9c882ab34bbc60dc297ca729504901479d5df7"},
|
||||
{file = "Cython-3.0.9-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:00b105b5d050645dd59e6767bc0f18b48a4aa11c85f42ec7dd8181606f4059e3"},
|
||||
{file = "Cython-3.0.9-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ac5536d09bef240cae0416d5a703d298b74c7bbc397da803ac9d344e732d4369"},
|
||||
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{file = "Cython-3.0.9-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:95ed792c966f969cea7489c32ff90150b415c1f3567db8d5a9d489c7c1602dac"},
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{file = "Cython-3.0.9-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:4cf0ed273bf60e97922fcbbdd380c39693922a597760160b4b4355e6078ca188"},
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{file = "Cython-3.0.9-cp38-cp38-win32.whl", hash = "sha256:5eb9bd4ae12ebb2bc79a193d95aacf090fbd8d7013e11ed5412711650cb34934"},
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{file = "Cython-3.0.9-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:4f836192140f033b2319a0128936367c295c2b32e23df05b03b672a6015757ea"},
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{file = "Cython-3.0.9-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:2fd198c1a7f8e9382904d622cc0efa3c184605881fd5262c64cbb7168c4c1ec5"},
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{file = "Cython-3.0.9-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:a274fe9ca5c53fafbcf5c8f262f8ad6896206a466f0eeb40aaf36a7951e957c0"},
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{file = "Cython-3.0.9-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:158c38360bbc5063341b1e78d3737f1251050f89f58a3df0d10fb171c44262be"},
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{file = "Cython-3.0.9-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:8bf30b045f7deda0014b042c1b41c1d272facc762ab657529e3b05505888e878"},
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{file = "Cython-3.0.9-cp39-cp39-win32.whl", hash = "sha256:9a001fd95c140c94d934078544ff60a3c46aca2dc86e75a76e4121d3cd1f4b33"},
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||||
{file = "Cython-3.0.9-cp39-cp39-win_amd64.whl", hash = "sha256:530c01c4aebba709c0ec9c7ecefe07177d0b9fd7ffee29450a118d92192ccbdf"},
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||||
{file = "Cython-3.0.9-py2.py3-none-any.whl", hash = "sha256:bf96417714353c5454c2e3238fca9338599330cf51625cdc1ca698684465646f"},
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||||
{file = "Cython-3.0.9.tar.gz", hash = "sha256:a2d354f059d1f055d34cfaa62c5b68bc78ac2ceab6407148d47fb508cf3ba4f3"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "debugpy"
|
||||
version = "1.8.1"
|
||||
|
|
@ -639,6 +572,17 @@ files = [
|
|||
[package.extras]
|
||||
test = ["pytest (>=6)"]
|
||||
|
||||
[[package]]
|
||||
name = "farama-notifications"
|
||||
version = "0.0.4"
|
||||
description = "Notifications for all Farama Foundation maintained libraries."
|
||||
optional = false
|
||||
python-versions = "*"
|
||||
files = [
|
||||
{file = "Farama-Notifications-0.0.4.tar.gz", hash = "sha256:13fceff2d14314cf80703c8266462ebf3733c7d165336eee998fc58e545efd18"},
|
||||
{file = "Farama_Notifications-0.0.4-py3-none-any.whl", hash = "sha256:14de931035a41961f7c056361dc7f980762a143d05791ef5794a751a2caf05ae"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "fasteners"
|
||||
version = "0.19"
|
||||
|
|
@ -877,6 +821,59 @@ files = [
|
|||
{file = "gym_notices-0.0.8-py3-none-any.whl", hash = "sha256:e5f82e00823a166747b4c2a07de63b6560b1acb880638547e0cabf825a01e463"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "gymnasium"
|
||||
version = "0.29.1"
|
||||
description = "A standard API for reinforcement learning and a diverse set of reference environments (formerly Gym)."
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "gymnasium-0.29.1-py3-none-any.whl", hash = "sha256:61c3384b5575985bb7f85e43213bcb40f36fcdff388cae6bc229304c71f2843e"},
|
||||
{file = "gymnasium-0.29.1.tar.gz", hash = "sha256:1a532752efcb7590478b1cc7aa04f608eb7a2fdad5570cd217b66b6a35274bb1"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
cloudpickle = ">=1.2.0"
|
||||
farama-notifications = ">=0.0.1"
|
||||
numpy = ">=1.21.0"
|
||||
typing-extensions = ">=4.3.0"
|
||||
|
||||
[package.extras]
|
||||
accept-rom-license = ["autorom[accept-rom-license] (>=0.4.2,<0.5.0)"]
|
||||
all = ["box2d-py (==2.3.5)", "cython (<3)", "imageio (>=2.14.1)", "jax (>=0.4.0)", "jaxlib (>=0.4.0)", "lz4 (>=3.1.0)", "matplotlib (>=3.0)", "moviepy (>=1.0.0)", "mujoco (>=2.3.3)", "mujoco-py (>=2.1,<2.2)", "opencv-python (>=3.0)", "pygame (>=2.1.3)", "shimmy[atari] (>=0.1.0,<1.0)", "swig (==4.*)", "torch (>=1.0.0)"]
|
||||
atari = ["shimmy[atari] (>=0.1.0,<1.0)"]
|
||||
box2d = ["box2d-py (==2.3.5)", "pygame (>=2.1.3)", "swig (==4.*)"]
|
||||
classic-control = ["pygame (>=2.1.3)", "pygame (>=2.1.3)"]
|
||||
jax = ["jax (>=0.4.0)", "jaxlib (>=0.4.0)"]
|
||||
mujoco = ["imageio (>=2.14.1)", "mujoco (>=2.3.3)"]
|
||||
mujoco-py = ["cython (<3)", "cython (<3)", "mujoco-py (>=2.1,<2.2)", "mujoco-py (>=2.1,<2.2)"]
|
||||
other = ["lz4 (>=3.1.0)", "matplotlib (>=3.0)", "moviepy (>=1.0.0)", "opencv-python (>=3.0)", "torch (>=1.0.0)"]
|
||||
testing = ["pytest (==7.1.3)", "scipy (>=1.7.3)"]
|
||||
toy-text = ["pygame (>=2.1.3)", "pygame (>=2.1.3)"]
|
||||
|
||||
[[package]]
|
||||
name = "gymnasium-robotics"
|
||||
version = "1.2.4"
|
||||
description = "Robotics environments for the Gymnasium repo."
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "gymnasium-robotics-1.2.4.tar.gz", hash = "sha256:d304192b066f8b800599dfbe3d9d90bba9b761ee884472bdc4d05968a8bc61cb"},
|
||||
{file = "gymnasium_robotics-1.2.4-py3-none-any.whl", hash = "sha256:c2cb23e087ca0280ae6802837eb7b3a6d14e5bd24c00803ab09f015fcff3eef5"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
gymnasium = ">=0.26"
|
||||
imageio = "*"
|
||||
Jinja2 = ">=3.0.3"
|
||||
mujoco = ">=2.3.3,<3.0"
|
||||
numpy = ">=1.21.0"
|
||||
PettingZoo = ">=1.23.0"
|
||||
|
||||
[package.extras]
|
||||
mujoco-py = ["cython (<3)", "mujoco-py (>=2.1,<2.2)"]
|
||||
testing = ["Jinja2 (>=3.0.3)", "PettingZoo (>=1.23.0)", "cython (<3)", "mujoco-py (>=2.1,<2.2)", "pytest (==7.0.1)"]
|
||||
|
||||
[[package]]
|
||||
name = "h5py"
|
||||
version = "3.10.0"
|
||||
|
|
@ -1506,25 +1503,6 @@ glfw = "*"
|
|||
numpy = "*"
|
||||
pyopengl = "*"
|
||||
|
||||
[[package]]
|
||||
name = "mujoco-py"
|
||||
version = "2.1.2.14"
|
||||
description = ""
|
||||
optional = false
|
||||
python-versions = ">=3.6"
|
||||
files = [
|
||||
{file = "mujoco-py-2.1.2.14.tar.gz", hash = "sha256:eb5b14485acf80a3cf8c15f4b080c6a28a9f79e68869aa696d16cbd51ea7706f"},
|
||||
{file = "mujoco_py-2.1.2.14-py3-none-any.whl", hash = "sha256:37c0b41bc0153a8a0eb3663103a67c60f65467753f74e4ff6e68b879f3e3a71f"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
cffi = ">=1.10"
|
||||
Cython = ">=0.27.2"
|
||||
fasteners = ">=0.15,<1.0"
|
||||
glfw = ">=1.4.0"
|
||||
imageio = ">=2.1.2"
|
||||
numpy = ">=1.11"
|
||||
|
||||
[[package]]
|
||||
name = "networkx"
|
||||
version = "3.2.1"
|
||||
|
|
@ -1940,6 +1918,31 @@ sql-other = ["SQLAlchemy (>=2.0.0)", "adbc-driver-postgresql (>=0.8.0)", "adbc-d
|
|||
test = ["hypothesis (>=6.46.1)", "pytest (>=7.3.2)", "pytest-xdist (>=2.2.0)"]
|
||||
xml = ["lxml (>=4.9.2)"]
|
||||
|
||||
[[package]]
|
||||
name = "pettingzoo"
|
||||
version = "1.24.3"
|
||||
description = "Gymnasium for multi-agent reinforcement learning."
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "pettingzoo-1.24.3-py3-none-any.whl", hash = "sha256:23ed90517d2e8a7098bdaf5e31234b3a7f7b73ca578d70d1ca7b9d0cb0e37982"},
|
||||
{file = "pettingzoo-1.24.3.tar.gz", hash = "sha256:91f9094f18e06fb74b98f4099cd22e8ae4396125e51719d50b30c9f1c7ab07e6"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
gymnasium = ">=0.28.0"
|
||||
numpy = ">=1.21.0"
|
||||
|
||||
[package.extras]
|
||||
all = ["box2d-py (==2.3.5)", "chess (==1.9.4)", "multi-agent-ale-py (==0.1.11)", "pillow (>=8.0.1)", "pygame (==2.3.0)", "pymunk (==6.2.0)", "rlcard (==1.0.5)", "scipy (>=1.4.1)", "shimmy[openspiel] (>=1.2.0)"]
|
||||
atari = ["multi-agent-ale-py (==0.1.11)", "pygame (==2.3.0)"]
|
||||
butterfly = ["pygame (==2.3.0)", "pymunk (==6.2.0)"]
|
||||
classic = ["chess (==1.9.4)", "pygame (==2.3.0)", "rlcard (==1.0.5)", "shimmy[openspiel] (>=1.2.0)"]
|
||||
mpe = ["pygame (==2.3.0)"]
|
||||
other = ["pillow (>=8.0.1)"]
|
||||
sisl = ["box2d-py (==2.3.5)", "pygame (==2.3.0)", "pymunk (==6.2.0)", "scipy (>=1.4.1)"]
|
||||
testing = ["AutoROM", "pre-commit", "pynput", "pytest", "pytest-cov", "pytest-markdown-docs", "pytest-xdist"]
|
||||
|
||||
[[package]]
|
||||
name = "pillow"
|
||||
version = "10.2.0"
|
||||
|
|
@ -3510,4 +3513,4 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p
|
|||
[metadata]
|
||||
lock-version = "2.0"
|
||||
python-versions = "^3.10"
|
||||
content-hash = "1a45c808e1c48bcbf4319d4cf6876771b7d50f40a5a8968a8b7f3af36192bf34"
|
||||
content-hash = "abe6fc1c5b99d6f51f2efb0adda0e7cd1fcfe7b2d789879dafa441869e555745"
|
||||
|
|
|
|||
|
|
@ -21,7 +21,6 @@ packages = [{include = "lerobot"}]
|
|||
|
||||
[tool.poetry.dependencies]
|
||||
python = "^3.10"
|
||||
cython = "^3.0.8"
|
||||
termcolor = "^2.4.0"
|
||||
omegaconf = "^2.3.0"
|
||||
dm-env = "^1.6"
|
||||
|
|
@ -43,7 +42,6 @@ torch = "^2.2.1"
|
|||
tensordict = {git = "https://github.com/pytorch/tensordict"}
|
||||
torchrl = {git = "https://github.com/pytorch/rl", rev = "13bef426dcfa5887c6e5034a6e9697993fa92c37"}
|
||||
mujoco = "2.3.7"
|
||||
mujoco-py = "^2.1.2.14"
|
||||
gym = "^0.26.2"
|
||||
opencv-python = "^4.9.0.80"
|
||||
diffusers = "^0.26.3"
|
||||
|
|
@ -52,6 +50,7 @@ h5py = "^3.10.0"
|
|||
dm-control = "1.0.14"
|
||||
huggingface-hub = {extras = ["hf-transfer"], version = "^0.21.4"}
|
||||
robomimic = "0.2.0"
|
||||
gymnasium-robotics = "^1.2.4"
|
||||
|
||||
|
||||
[tool.poetry.group.dev.dependencies]
|
||||
|
|
|
|||
|
|
@ -7,7 +7,7 @@ from lerobot.common.datasets.factory import make_offline_buffer
|
|||
|
||||
from lerobot.common.envs.factory import make_env
|
||||
from lerobot.common.envs.pusht.env import PushtEnv
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from lerobot.common.envs.simxarm import SimxarmEnv
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from lerobot.common.envs.simxarm.env import SimxarmEnv
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from .utils import DEVICE, init_config
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Loading…
Reference in New Issue