scripts/graspnet_task_e/debug_solution_pca_perception.py

"""Compare submit-style RGB-D PCA estimates against Task-E simulator truth.

This script uses simulator object roots only for debugging/calibration.  It is
not part of the submission policy.
"""

from __future__ import annotations

import argparse
import os
import sys

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("--settle_steps", type=int, default=5)
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 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
import solution_pca  # noqa: 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)

    policy = solution_pca.AlgSolution()
    try:
        obs, _ = env.reset(seed=args_cli.seed)
        zero = torch.zeros((1, 8), dtype=torch.float32, device=args_cli.device)
        for _ in range(max(0, args_cli.settle_steps)):
            obs, *_ = env.step(zero)

        rgb, depth = policy._video_rgb_depth(obs)
        print(f"[PERCEPTION_DEBUG] seed={args_cli.seed} settle_steps={args_cli.settle_steps}")
        for obj_idx in (1, 2, 3):
            est, rot = policy._estimate_grasp(rgb, depth, obj_idx)
            pick_xy = est[:2] + solution_pca.OBJ_GRASP_CENTER_OFFSETS[obj_idx]
            obj = env.unwrapped.scene.rigid_objects[f"object_{obj_idx}"]
            truth = obj.data.root_pos_w[0].detach().cpu().numpy().astype(np.float64)
            delta = est - truth
            pick_delta = np.r_[pick_xy - truth[:2], est[2] - truth[2]]
            jaw_yaw = float(np.arctan2(rot[1, 1], rot[0, 1]))
            print(
                "[PERCEPTION_DEBUG] "
                f"obj={obj_idx} truth=({truth[0]:.4f},{truth[1]:.4f},{truth[2]:.4f}) "
                f"est=({est[0]:.4f},{est[1]:.4f},{est[2]:.4f}) "
                f"delta=({delta[0]:+.4f},{delta[1]:+.4f},{delta[2]:+.4f}) "
                f"pick_delta=({pick_delta[0]:+.4f},{pick_delta[1]:+.4f},{pick_delta[2]:+.4f}) "
                f"jaw_yaw={jaw_yaw:+.3f}"
            )
    finally:
        env.close()


if __name__ == "__main__":
    try:
        main()
    finally:
        simulation_app.close()