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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
from .errors import IsaacLabArenaError
def cleanup_isaaclab(env, simulation_app) -> None:
"""Cleanup IsaacLab env and simulation app resources."""
# Ignore signals during cleanup to prevent interruption
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:
# Restore signal handlers
signal.signal(signal.SIGINT, old_sigint)
signal.signal(signal.SIGTERM, old_sigterm)
class IsaacLabEnvWrapper(gym.vector.AsyncVectorEnv):
"""Wrapper adapting IsaacLab batched GPU env to AsyncVectorEnv.
IsaacLab handles vectorization internally on GPU. We inherit from
AsyncVectorEnv for compatibility with LeRobot."""
metadata = {"render_modes": ["rgb_array"], "render_fps": 30}
_cleanup_in_progress = False # Class-level flag for re-entrant protection
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}
# Register cleanup handlers
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 # Prevent re-entrant cleanup
IsaacLabEnvWrapper._cleanup_in_progress = True
logging.info(f"Received signal {signum}, cleaning up...")
self._cleanup()
# Exit without raising to avoid propagating through callbacks
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)
# Convert to numpy for gym compatibility
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:
"""Render all environments and return list of frames."""
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 |