import importlib import logging from collections import deque from typing import Optional import einops import numpy as np import torch from tensordict import TensorDict from torchrl.data.tensor_specs import ( BoundedTensorSpec, CompositeSpec, DiscreteTensorSpec, UnboundedContinuousTensorSpec, ) from torchrl.envs.libs.gym import _gym_to_torchrl_spec_transform from lerobot.common.envs.abstract import AbstractEnv from lerobot.common.utils import set_global_seed MAX_NUM_ACTIONS = 4 _has_gym = importlib.util.find_spec("gymnasium") is not None class SimxarmEnv(AbstractEnv): name = "simxarm" available_tasks = ["lift"] def __init__( self, task, frame_skip: int = 1, from_pixels: bool = False, pixels_only: bool = False, image_size=None, seed=1337, device="cpu", num_prev_obs=0, num_prev_action=0, ): super().__init__( task=task, frame_skip=frame_skip, from_pixels=from_pixels, pixels_only=pixels_only, image_size=image_size, seed=seed, device=device, num_prev_obs=num_prev_obs, num_prev_action=num_prev_action, ) def _make_env(self): if not _has_gym: raise ImportError("Cannot import gymnasium.") import gymnasium from lerobot.common.envs.simxarm.simxarm import TASKS if self.task not in TASKS: raise ValueError(f"Unknown task {self.task}. Must be one of {list(TASKS.keys())}") self._env = TASKS[self.task]["env"]() num_actions = len(TASKS[self.task]["action_space"]) self._action_space = gymnasium.spaces.Box(low=-1.0, high=1.0, shape=(num_actions,)) self._action_padding = np.zeros((MAX_NUM_ACTIONS - num_actions), dtype=np.float32) if "w" not in TASKS[self.task]["action_space"]: self._action_padding[-1] = 1.0 def render(self, mode="rgb_array", width=384, height=384): return self._env.render(mode, width=width, height=height) def _format_raw_obs(self, raw_obs): if self.from_pixels: image = self.render(mode="rgb_array", width=self.image_size, height=self.image_size) image = image.transpose(2, 0, 1) # (H, W, C) -> (C, H, W) image = torch.tensor(image.copy(), dtype=torch.uint8) obs = {"image": image} if not self.pixels_only: obs["state"] = torch.tensor(self._env.robot_state, dtype=torch.float32) else: obs = {"state": torch.tensor(raw_obs["observation"], dtype=torch.float32)} # obs = TensorDict(obs, batch_size=[]) return obs def _reset(self, tensordict: Optional[TensorDict] = None): td = tensordict if td is None or td.is_empty(): raw_obs = self._env.reset() obs = self._format_raw_obs(raw_obs) if self.num_prev_obs > 0: stacked_obs = {} if "image" in obs: self._prev_obs_image_queue = deque( [obs["image"]] * (self.num_prev_obs + 1), maxlen=(self.num_prev_obs + 1) ) stacked_obs["image"] = torch.stack(list(self._prev_obs_image_queue)) if "state" in obs: self._prev_obs_state_queue = deque( [obs["state"]] * (self.num_prev_obs + 1), maxlen=(self.num_prev_obs + 1) ) stacked_obs["state"] = torch.stack(list(self._prev_obs_state_queue)) obs = stacked_obs td = TensorDict( { "observation": TensorDict(obs, batch_size=[]), "done": torch.tensor([False], dtype=torch.bool), }, batch_size=[], ) else: raise NotImplementedError() return td def _step(self, tensordict: TensorDict): td = tensordict action = td["action"].numpy() # step expects shape=(4,) so we pad if necessary action = np.concatenate([action, self._action_padding]) # TODO(rcadene): add info["is_success"] and info["success"] ? sum_reward = 0 if action.ndim == 1: action = einops.repeat(action, "c -> t c", t=self.frame_skip) else: if self.frame_skip > 1: raise NotImplementedError() num_action_steps = action.shape[0] for i in range(num_action_steps): raw_obs, reward, done, info = self._env.step(action[i]) sum_reward += reward obs = self._format_raw_obs(raw_obs) if self.num_prev_obs > 0: stacked_obs = {} if "image" in obs: self._prev_obs_image_queue.append(obs["image"]) stacked_obs["image"] = torch.stack(list(self._prev_obs_image_queue)) if "state" in obs: self._prev_obs_state_queue.append(obs["state"]) stacked_obs["state"] = torch.stack(list(self._prev_obs_state_queue)) obs = stacked_obs td = TensorDict( { "observation": self._format_raw_obs(raw_obs), "reward": torch.tensor([sum_reward], dtype=torch.float32), "done": torch.tensor([done], dtype=torch.bool), "success": torch.tensor([info["success"]], dtype=torch.bool), }, batch_size=[], ) return td def _make_spec(self): obs = {} if self.from_pixels: image_shape = (3, self.image_size, self.image_size) if self.num_prev_obs > 0: image_shape = (self.num_prev_obs + 1, *image_shape) obs["image"] = BoundedTensorSpec( low=0, high=255, shape=image_shape, dtype=torch.uint8, device=self.device, ) if not self.pixels_only: state_shape = (len(self._env.robot_state),) if self.num_prev_obs > 0: state_shape = (self.num_prev_obs + 1, *state_shape) obs["state"] = UnboundedContinuousTensorSpec( shape=state_shape, dtype=torch.float32, device=self.device, ) else: # TODO(rcadene): add observation_space achieved_goal and desired_goal? state_shape = self._env.observation_space["observation"].shape if self.num_prev_obs > 0: state_shape = (self.num_prev_obs + 1, *state_shape) obs["state"] = UnboundedContinuousTensorSpec( # TODO: shape=state_shape, dtype=torch.float32, device=self.device, ) self.observation_spec = CompositeSpec({"observation": obs}) self.action_spec = _gym_to_torchrl_spec_transform( self._action_space, device=self.device, ) self.reward_spec = UnboundedContinuousTensorSpec( shape=(1,), dtype=torch.float32, device=self.device, ) self.done_spec = CompositeSpec( { "done": DiscreteTensorSpec( 2, shape=(1,), dtype=torch.bool, device=self.device, ), "success": DiscreteTensorSpec( 2, shape=(1,), dtype=torch.bool, device=self.device, ), } ) def _set_seed(self, seed: Optional[int]): set_global_seed(seed) self._seed = seed # TODO(aliberts): change self._reset so that it takes in a seed value logging.warning("simxarm env is not properly seeded")