"""Measure execution error for demo.solution_pca in Task E. Uses simulator internals only for debugging: object root, gripper_base, link7, and link8 positions. The submission policy still only receives observations. """ from __future__ import annotations import argparse import os import sys import time from isaaclab.app import AppLauncher parser = argparse.ArgumentParser() parser.add_argument("--task", type=str, default="ATEC-TaskE-Piper") parser.add_argument("--seed", type=int, default=12) parser.add_argument("--object", type=int, default=3) parser.add_argument("--max_steps", type=int, default=2500) parser.add_argument("--solution_module", type=str, default="solution_pca") 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) app_launcher = AppLauncher(args_cli) simulation_app = app_launcher.app import gymnasium as gym # noqa: E402 import importlib # noqa: E402 import numpy as np # 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 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) module = importlib.import_module(args_cli.solution_module) AlgSolution = module.AlgSolution policy = AlgSolution() try: obs, _ = env.reset(seed=args_cli.seed) policy.reset_episode() robot = env.unwrapped.scene.articulations["robot"] obj = env.unwrapped.scene.rigid_objects[f"object_{args_cli.object}"] gb_id = int(robot.find_bodies("gripper_base")[0][0]) link7_id = int(robot.find_bodies("link7")[0][0]) link8_id = int(robot.find_bodies("link8")[0][0]) obj_initial = obj.data.root_pos_w[0].detach().cpu().numpy().astype(np.float64) best = { "finger_dist": (999.0, None, 0, None), "gb_dist": (999.0, None, 0, None), "max_z_gain": (-999.0, None, 0, None), "pin_finger_err": (999.0, None, 0, None), "pin_gb_err": (999.0, None, 0, None), } stage_best = {} pin_err_sum = {"finger": 0.0, "gb": 0.0} pin_err_n = 0 total_reward = 0.0 start = time.time() for step in range(args_cli.max_steps): 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 obj_pos = obj.data.root_pos_w[0, :3].detach().cpu().numpy().astype(np.float64) gb = robot.data.body_pos_w[0, gb_id, :3].detach().cpu().numpy().astype(np.float64) f7 = robot.data.body_pos_w[0, link7_id, :3].detach().cpu().numpy().astype(np.float64) f8 = robot.data.body_pos_w[0, link8_id, :3].detach().cpu().numpy().astype(np.float64) finger = 0.5 * (f7 + f8) qpos = policy._obs_qpos(obs) pin_finger = policy.ik.finger_center_world(qpos) pin_gb_b, _ = policy.ik.fk_base(qpos[:6]) pin_gb = module.BASE_POS_W + module.R_W_B @ pin_gb_b gap = float(np.linalg.norm(f7 - f8)) fd = float(np.linalg.norm(finger - obj_pos)) gd = float(np.linalg.norm(gb - obj_pos)) pfe = float(np.linalg.norm(pin_finger - finger)) pge = float(np.linalg.norm(pin_gb - gb)) pin_err_sum["finger"] += pfe pin_err_sum["gb"] += pge pin_err_n += 1 zg = float(obj_pos[2] - obj_initial[2]) plan_idx = getattr(policy, "plan_idx", -1) target_step = getattr(policy, "step_in_target", -1) label = "none" plan = getattr(policy, "plan", None) if isinstance(plan, list) and 0 <= plan_idx < len(plan): label = getattr(plan[plan_idx], "label", str(plan_idx)) stage = stage_best.setdefault( label, { "min_finger": (999.0, None, 0, None), "max_z_gain": (-999.0, None, 0, None), "min_gap": (999.0, 0), "last": None, }, ) if fd < stage["min_finger"][0]: stage["min_finger"] = (fd, finger - obj_pos, step, (plan_idx, target_step, gap, qpos[6], qpos[7])) if zg > stage["max_z_gain"][0]: stage["max_z_gain"] = (zg, obj_pos.copy(), step, (plan_idx, target_step, gap, qpos[6], qpos[7])) if gap < stage["min_gap"][0]: stage["min_gap"] = (gap, step) stage["last"] = (finger - obj_pos, obj_pos.copy(), gap, qpos[6], qpos[7], step) if fd < best["finger_dist"][0]: best["finger_dist"] = (fd, finger - obj_pos, step, (plan_idx, target_step, gap)) if gd < best["gb_dist"][0]: best["gb_dist"] = (gd, gb - obj_pos, step, (plan_idx, target_step, gap)) if zg > best["max_z_gain"][0]: best["max_z_gain"] = (zg, obj_pos.copy(), step, (plan_idx, target_step, gap)) if pfe < best["pin_finger_err"][0]: best["pin_finger_err"] = (pfe, pin_finger - finger, step, (plan_idx, target_step, gap)) if pge < best["pin_gb_err"][0]: best["pin_gb_err"] = (pge, pin_gb - gb, step, (plan_idx, target_step, gap)) if bool(terminated.item() or truncated.item()): break final = obj.data.root_pos_w[0, :3].detach().cpu().numpy().astype(np.float64) print(f"[EXEC_DEBUG] seed={args_cli.seed} obj={args_cli.object} score={total_reward:.2f} wall={time.time()-start:.1f}s") print(f"[EXEC_DEBUG] initial=({obj_initial[0]:.4f},{obj_initial[1]:.4f},{obj_initial[2]:.4f}) final=({final[0]:.4f},{final[1]:.4f},{final[2]:.4f})") if pin_err_n: print(f"[EXEC_DEBUG] pin_mean_err finger={pin_err_sum['finger']/pin_err_n:.4f} gb={pin_err_sum['gb']/pin_err_n:.4f}") for key, (value, vec, step, meta) in best.items(): if vec is None: print(f"[EXEC_DEBUG] {key}=none") elif key == "max_z_gain": print(f"[EXEC_DEBUG] {key}={value:.4f} obj=({vec[0]:.4f},{vec[1]:.4f},{vec[2]:.4f}) step={step} meta={meta}") else: print(f"[EXEC_DEBUG] {key}={value:.4f} vec=({vec[0]:+.4f},{vec[1]:+.4f},{vec[2]:+.4f}) step={step} meta={meta}") for label, stats in stage_best.items(): if not any(k in label for k in ("reach", "insert", "close", "lift", "mid", "release")): continue fd, fvec, fstep, fmeta = stats["min_finger"] zg, zobj, zstep, zmeta = stats["max_z_gain"] last = stats["last"] if fvec is None or zobj is None or last is None: continue lvec, lobj, lgap, lq7, lq8, lstep = last print( f"[STAGE_DEBUG] label={label} min_fd={fd:.4f} " f"min_vec=({fvec[0]:+.4f},{fvec[1]:+.4f},{fvec[2]:+.4f}) " f"min_meta={fmeta} max_z_gain={zg:.4f} " f"z_obj=({zobj[0]:.4f},{zobj[1]:.4f},{zobj[2]:.4f}) z_meta={zmeta} " f"last_vec=({lvec[0]:+.4f},{lvec[1]:+.4f},{lvec[2]:+.4f}) " f"last_obj=({lobj[0]:.4f},{lobj[1]:.4f},{lobj[2]:.4f}) " f"last_gap={lgap:.4f} last_q=({lq7:.4f},{lq8:.4f}) last_step={lstep}" ) finally: env.close() if __name__ == "__main__": try: main() finally: simulation_app.close()