File size: 3,084 Bytes
21e1acb | 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 | """Trace Task-E policy rollout with object positions at fixed intervals."""
import argparse
import os
import sys
from isaaclab.app import AppLauncher
parser = argparse.ArgumentParser()
parser.add_argument("--checkpoint", required=True)
parser.add_argument("--solution_module", default="solution_act")
parser.add_argument("--task", default="ATEC-TaskE-Piper")
parser.add_argument("--seed", type=int, default=11)
parser.add_argument("--max_steps", type=int, default=1800)
parser.add_argument("--interval", type=int, default=100)
AppLauncher.add_app_launcher_args(parser)
args_cli = parser.parse_args()
args_cli.enable_cameras = True
repo_root = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", ".."))
demo_dir = os.path.join(repo_root, "demo")
if repo_root not in sys.path:
sys.path.insert(0, repo_root)
if demo_dir not in sys.path:
sys.path.insert(0, demo_dir)
os.environ["ATEC_ACT_POLICY_PATH"] = os.path.abspath(args_cli.checkpoint)
app_launcher = AppLauncher(args_cli)
simulation_app = app_launcher.app
import importlib # noqa: E402
import gymnasium as gym # noqa: E402
import torch # noqa: E402
from isaaclab.envs import DirectMARLEnv, multi_agent_to_single_agent # noqa: E402
from isaaclab_tasks.utils import parse_env_cfg # noqa: E402
import atec_rl_lab.tasks # noqa: F401,E402
from scripts.act.task_e.collector import basket_status_lines # noqa: E402
def _object_summary(env) -> str:
return " | ".join(basket_status_lines(env, [1, 2, 3]))
def main() -> None:
env_cfg = parse_env_cfg(args_cli.task, device=args_cli.device, num_envs=1)
env_cfg.seed = args_cli.seed
env = gym.make(args_cli.task, cfg=env_cfg)
if isinstance(env.unwrapped, DirectMARLEnv):
env = multi_agent_to_single_agent(env)
policy = importlib.import_module(args_cli.solution_module).AlgSolution()
obs, _ = env.reset(seed=args_cli.seed)
policy.reset_episode()
total_reward = 0.0
try:
print(f"[TRACE_STEP] step=0 score=0.00 {_object_summary(env)}", flush=True)
for step in range(1, args_cli.max_steps + 1):
with torch.inference_mode():
resp = policy.predicts(obs, total_reward)
action = torch.as_tensor(resp["action"], dtype=torch.float32, device=args_cli.device).view(1, -1)
obs, reward, terminated, truncated, info = env.step(action)
sim_dt = info["Step_dt"]
total_reward += reward.mean().item() / sim_dt if isinstance(reward, torch.Tensor) else float(reward) / sim_dt
if step % args_cli.interval == 0 or bool(terminated.item() or truncated.item()):
print(
f"[TRACE_STEP] step={step} score={total_reward:.2f} {_object_summary(env)}",
flush=True,
)
if bool(terminated.item() or truncated.item()):
break
print(f"[RESULT] score={total_reward:.2f} steps={step}", flush=True)
finally:
env.close()
if __name__ == "__main__":
try:
main()
finally:
simulation_app.close()
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