File size: 6,837 Bytes
1e09dca | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 | """VectorEnv wrapper adapting Isaac Lab's GPU-batched env for LeRobot EnvHub."""
from __future__ import annotations
import atexit
import logging
import os
import signal
from contextlib import suppress
from typing import Any
import gymnasium as gym
import numpy as np
import torch
try:
from .errors import IsaacLabArenaError
except ImportError:
from errors import IsaacLabArenaError
def cleanup_isaaclab(env, simulation_app) -> None:
"""Close the gym env and Kit app; ignore signals mid-teardown."""
old_sigint = signal.signal(signal.SIGINT, signal.SIG_IGN)
old_sigterm = signal.signal(signal.SIGTERM, signal.SIG_IGN)
try:
with suppress(Exception):
if env is not None:
env.close()
with suppress(Exception):
if simulation_app is not None:
simulation_app.app.close()
finally:
signal.signal(signal.SIGINT, old_sigint)
signal.signal(signal.SIGTERM, old_sigterm)
class IsaacLabEnvWrapper(gym.vector.AsyncVectorEnv):
"""Isaac Lab already vectorizes on GPU; this exposes an AsyncVectorEnv API."""
metadata = {"render_modes": ["rgb_array"], "render_fps": 15}
_cleanup_in_progress = False
def __init__(
self,
env,
episode_length: int = 500,
task: str | None = None,
render_mode: str | None = "rgb_array",
simulation_app=None,
):
self._env = env
self._num_envs = env.num_envs
self._episode_length = episode_length
self._closed = False
self.render_mode = render_mode
self._simulation_app = simulation_app
self.observation_space = env.observation_space
self.action_space = env.action_space
self.single_observation_space = env.observation_space
self.single_action_space = env.action_space
self.task = task
if hasattr(env, "metadata") and env.metadata:
self.metadata = {**self.metadata, **env.metadata}
atexit.register(self._cleanup)
signal.signal(signal.SIGINT, self._signal_handler)
signal.signal(signal.SIGTERM, self._signal_handler)
def _signal_handler(self, signum, frame):
if IsaacLabEnvWrapper._cleanup_in_progress:
return
IsaacLabEnvWrapper._cleanup_in_progress = True
logging.info(f"Received signal {signum}, cleaning up...")
self._cleanup()
os._exit(0)
def _check_closed(self):
if self._closed:
raise IsaacLabArenaError()
@property
def unwrapped(self):
return self
@property
def num_envs(self) -> int:
return self._num_envs
@property
def _max_episode_steps(self) -> int:
return self._episode_length
@property
def device(self) -> str:
return getattr(self._env, "device", "cpu")
def reset(
self,
*,
seed: int | list[int] | None = None,
options: dict[str, Any] | None = None,
) -> tuple[dict[str, Any], dict[str, Any]]:
self._check_closed()
if isinstance(seed, (list, tuple, range)):
seed = seed[0] if len(seed) > 0 else None
obs, info = self._env.reset(seed=seed, options=options)
if "final_info" not in info:
zeros = np.zeros(self._num_envs, dtype=bool)
info["final_info"] = {"is_success": zeros}
return obs, info
def step(
self, actions: np.ndarray | torch.Tensor
) -> tuple[dict, np.ndarray, np.ndarray, np.ndarray, dict]:
self._check_closed()
if isinstance(actions, np.ndarray):
actions = torch.from_numpy(actions).to(self._env.device)
obs, reward, terminated, truncated, info = self._env.step(actions)
reward = reward.cpu().numpy().astype(np.float32)
terminated = terminated.cpu().numpy().astype(bool)
truncated = truncated.cpu().numpy().astype(bool)
is_success = self._get_success(terminated, truncated)
info["final_info"] = {"is_success": is_success}
return obs, reward, terminated, truncated, info
def _get_success(self, terminated: np.ndarray, truncated: np.ndarray) -> np.ndarray:
is_success = np.zeros(self._num_envs, dtype=bool)
if not hasattr(self._env, "termination_manager"):
return is_success & (terminated | truncated)
term_manager = self._env.termination_manager
if not hasattr(term_manager, "get_term"):
return is_success & (terminated | truncated)
success_tensor = term_manager.get_term("success")
if success_tensor is None:
return is_success & (terminated | truncated)
is_success = success_tensor.cpu().numpy().astype(bool)
return is_success & (terminated | truncated)
def call(self, method_name: str, *args, **kwargs) -> list[Any]:
if method_name == "_max_episode_steps":
return [self._episode_length] * self._num_envs
if method_name == "task":
return [self.task] * self._num_envs
if method_name == "render":
return self.render_all()
if hasattr(self._env, method_name):
attr = getattr(self._env, method_name)
result = attr(*args, **kwargs) if callable(attr) else attr
if isinstance(result, list):
return result
return [result] * self._num_envs
raise AttributeError(f"IsaacLab-Arena has no method/attribute '{method_name}'")
def render_all(self) -> list[np.ndarray]:
self._check_closed()
frames = self.render()
if frames is None:
placeholder = np.zeros((480, 640, 3), dtype=np.uint8)
return [placeholder] * self._num_envs
return [frames] * self._num_envs
def render(self) -> np.ndarray | None:
self._check_closed()
if self.render_mode != "rgb_array":
return None
frames = self._env.render() if hasattr(self._env, "render") else None
if frames is None:
return None
if isinstance(frames, torch.Tensor):
frames = frames.cpu().numpy()
return frames[0] if frames.ndim == 4 else frames
def _cleanup(self) -> None:
if self._closed:
return
self._closed = True
IsaacLabEnvWrapper._cleanup_in_progress = True
logging.info("Cleaning up IsaacLab Arena environment...")
cleanup_isaaclab(self._env, self._simulation_app)
def close(self) -> None:
self._cleanup()
@property
def envs(self) -> list[IsaacLabEnvWrapper]:
return [self] * self._num_envs
def __del__(self):
self._cleanup()
def __enter__(self):
return self
def __exit__(self, exc_type, exc_val, exc_tb):
self._cleanup()
return False
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