WIP Upgrading simxam from mujoco-py to mujoco python bindings
This commit is contained in:
Simon Alibert
237
lerobot/common/envs/simxarm/env.py
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237
lerobot/common/envs/simxarm/env.py
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import importlib
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from collections import deque
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from typing import Optional
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import einops
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import numpy as np
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import torch
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from tensordict import TensorDict
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from torchrl.data.tensor_specs import (
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BoundedTensorSpec,
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CompositeSpec,
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DiscreteTensorSpec,
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UnboundedContinuousTensorSpec,
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)
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from torchrl.envs.libs.gym import _gym_to_torchrl_spec_transform
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from lerobot.common.envs.abstract import AbstractEnv
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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("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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def __init__(
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self,
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task,
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frame_skip: int = 1,
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from_pixels: bool = False,
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pixels_only: bool = False,
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image_size=None,
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seed=1337,
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device="cpu",
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num_prev_obs=0,
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num_prev_action=0,
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):
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super().__init__(
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task=task,
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frame_skip=frame_skip,
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from_pixels=from_pixels,
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pixels_only=pixels_only,
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image_size=image_size,
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seed=seed,
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device=device,
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num_prev_obs=num_prev_obs,
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num_prev_action=num_prev_action,
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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_gym:
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raise ImportError("Cannot import gym.")
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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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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 = 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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def render(self, mode="rgb_array", width=384, height=384):
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return self._env.render(mode, width=width, height=height)
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def _format_raw_obs(self, raw_obs):
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if self.from_pixels:
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image = self.render(mode="rgb_array", width=self.image_size, height=self.image_size)
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image = image.transpose(2, 0, 1) # (H, W, C) -> (C, H, W)
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image = torch.tensor(image.copy(), dtype=torch.uint8)
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obs = {"image": image}
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if not self.pixels_only:
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obs["state"] = torch.tensor(self._env.robot_state, dtype=torch.float32)
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else:
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obs = {"state": torch.tensor(raw_obs["observation"], dtype=torch.float32)}
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obs = TensorDict(obs, batch_size=[])
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return obs
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def _reset(self, tensordict: Optional[TensorDict] = None):
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td = tensordict
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if td is None or td.is_empty():
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raw_obs = self._env.reset()
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obs = self._format_raw_obs(raw_obs)
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if self.num_prev_obs > 0:
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stacked_obs = {}
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if "image" in obs:
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self._prev_obs_image_queue = deque(
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[obs["image"]] * (self.num_prev_obs + 1), maxlen=(self.num_prev_obs + 1)
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)
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stacked_obs["image"] = torch.stack(list(self._prev_obs_image_queue))
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if "state" in obs:
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self._prev_obs_state_queue = deque(
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[obs["state"]] * (self.num_prev_obs + 1), maxlen=(self.num_prev_obs + 1)
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)
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stacked_obs["state"] = torch.stack(list(self._prev_obs_state_queue))
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obs = stacked_obs
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td = TensorDict(
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{
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"observation": TensorDict(obs, batch_size=[]),
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"done": torch.tensor([False], dtype=torch.bool),
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},
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batch_size=[],
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)
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else:
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raise NotImplementedError()
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return td
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def _step(self, tensordict: TensorDict):
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td = tensordict
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action = td["action"].numpy()
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# step expects shape=(4,) so we pad if necessary
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action = np.concatenate([action, self._action_padding])
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# TODO(rcadene): add info["is_success"] and info["success"] ?
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sum_reward = 0
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if action.ndim == 1:
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action = einops.repeat(action, "c -> t c", t=self.frame_skip)
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else:
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if self.frame_skip > 1:
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raise NotImplementedError()
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num_action_steps = action.shape[0]
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for i in range(num_action_steps):
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raw_obs, reward, done, info = self._env.step(action[i])
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sum_reward += reward
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obs = self._format_raw_obs(raw_obs)
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if self.num_prev_obs > 0:
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stacked_obs = {}
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if "image" in obs:
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self._prev_obs_image_queue.append(obs["image"])
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stacked_obs["image"] = torch.stack(list(self._prev_obs_image_queue))
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if "state" in obs:
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self._prev_obs_state_queue.append(obs["state"])
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stacked_obs["state"] = torch.stack(list(self._prev_obs_state_queue))
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obs = stacked_obs
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td = TensorDict(
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{
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"observation": self._format_raw_obs(raw_obs),
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"reward": torch.tensor([sum_reward], dtype=torch.float32),
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"done": torch.tensor([done], dtype=torch.bool),
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"success": torch.tensor([info["success"]], dtype=torch.bool),
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},
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batch_size=[],
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)
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return td
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def _make_spec(self):
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obs = {}
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if self.from_pixels:
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image_shape = (3, self.image_size, self.image_size)
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if self.num_prev_obs > 0:
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image_shape = (self.num_prev_obs + 1, *image_shape)
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obs["image"] = BoundedTensorSpec(
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low=0,
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high=255,
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shape=image_shape,
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dtype=torch.uint8,
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device=self.device,
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)
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if not self.pixels_only:
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state_shape = (len(self._env.robot_state),)
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if self.num_prev_obs > 0:
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state_shape = (self.num_prev_obs + 1, *state_shape)
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obs["state"] = UnboundedContinuousTensorSpec(
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shape=state_shape,
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dtype=torch.float32,
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device=self.device,
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)
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else:
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# TODO(rcadene): add observation_space achieved_goal and desired_goal?
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state_shape = self._env.observation_space["observation"].shape
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if self.num_prev_obs > 0:
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state_shape = (self.num_prev_obs + 1, *state_shape)
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obs["state"] = UnboundedContinuousTensorSpec(
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# TODO:
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shape=state_shape,
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dtype=torch.float32,
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device=self.device,
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)
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self.observation_spec = CompositeSpec({"observation": obs})
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self.action_spec = _gym_to_torchrl_spec_transform(
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self._action_space,
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device=self.device,
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)
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self.reward_spec = UnboundedContinuousTensorSpec(
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shape=(1,),
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dtype=torch.float32,
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device=self.device,
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)
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self.done_spec = CompositeSpec(
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{
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"done": DiscreteTensorSpec(
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2,
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shape=(1,),
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dtype=torch.bool,
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device=self.device,
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),
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"success": DiscreteTensorSpec(
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2,
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shape=(1,),
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dtype=torch.bool,
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device=self.device,
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),
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}
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)
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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.action_space.seed(seed)
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# self.set_seed(seed)
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self._seed = seed
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