"""Scripted oracle data collection for Task E (pick-and-place). Usage ----- # NOTE: only for object 3 now, if you want to pick objects 1 and 2, please modify the distance accordingly. python scripts/act/collect_demos_task_e.py --pick_objects 3 --num_demos 50 --headless """ import argparse import os import sys # sys.path.insert(0, os.path.dirname(__file__)) from isaaclab.app import AppLauncher from cli_args import add_collect_demo_args parser = argparse.ArgumentParser(description="Collect Task E demonstrations for ACT.") add_collect_demo_args(parser) AppLauncher.add_app_launcher_args(parser) args_cli = parser.parse_args() if args_cli.save_video or args_cli.save_images: args_cli.enable_cameras = True app_launcher = AppLauncher(args_cli) simulation_app = app_launcher.app import h5py import json import numpy as np from isaaclab.actuators import ImplicitActuatorCfg from isaaclab.envs import ManagerBasedRLEnv from isaaclab.sensors import CameraCfg import isaaclab.sim as sim_utils from atec_rl_lab.tasks.task_e.env_cfg import TaskEEnvPiperCfg from atec_rl_lab.utils import CartesianController from task_e.config import ( EE_BODY_NAME, ARM_JOINT_NAMES, GRIPPER_JOINT_NAMES, ACT_STIFFNESS, ACT_DAMPING, ACT_EFFORT_LIMIT, ACT_VEL_LIMIT, CAM_H, CAM_W, CAM_POS, CAM_ROT, ) from task_e.collector import basket_status_lines, check_objects_in_basket, collect_one_demo def _trace_lines(data: dict | None, pick_objects: list[int]) -> list[str]: if data is None or "trace" not in data: return [] lines = [] for obj_idx in pick_objects: tr = data["trace"].get(f"object_{obj_idx}", {}) if not tr: continue states = tr.get("states", {}) close = states.get("CLOSE", {}) lift = states.get("LIFT", {}) transport = states.get("TRANSPORT", {}) def _vec(value): if value is None: return "none" return "(" + ",".join(f"{float(v):+.3f}" for v in value[:3]) + ")" lines.append( f"object_{obj_idx}: z_gain={float(tr.get('z_gain', 0.0)):.3f} " f"lifted={tr.get('lifted')} reward_lifted={tr.get('reward_lifted')} " f"gap_close={float(close.get('min_gripper_gap', 999.0)):.3f} " f"finger_close={float(close.get('min_finger_center_dist', 999.0)):.3f} " f"finger_vec_close={_vec(close.get('min_finger_center_vec'))} " f"gap_lift={float(lift.get('min_gripper_gap', 999.0)):.3f} " f"finger_gap={float(lift.get('min_finger_body_gap', 999.0)):.3f} " f"finger_lift={float(lift.get('min_finger_center_dist', 999.0)):.3f} " f"finger_vec_lift={_vec(lift.get('min_finger_center_vec'))} " f"finger_transport={float(transport.get('min_finger_center_dist', 999.0)):.3f} " f"ee_vec_lift={_vec(lift.get('min_ee_vec'))} " f"basket_inside={tr.get('basket_inside')}" ) return lines def build_env(pick_objects: list[int], need_camera: bool) -> ManagerBasedRLEnv: import time cfg = TaskEEnvPiperCfg() cfg.seed = int(time.time_ns() % (2**31)) # random seed each call cfg.scene.num_envs = 1 cfg.episode_length_s = 80.0 * len(pick_objects) + 30.0 if not need_camera: cfg.scene.video_cam = None cfg.scene.ee_camera = None cfg.scene.ee_dual_camera = None cfg.scene.head_camera = None cfg.observations.image = None cfg.scene.robot.actuators["default"] = ImplicitActuatorCfg( joint_names_expr=[".*"], effort_limit=ACT_EFFORT_LIMIT, velocity_limit=ACT_VEL_LIMIT, stiffness=ACT_STIFFNESS, damping=ACT_DAMPING, ) # if need_camera: # cfg.scene.video_cam = CameraCfg( # prim_path="{ENV_REGEX_NS}/video_cam", # update_period=0.0, # height=CAM_H, width=CAM_W, # data_types=["rgb"], # spawn=sim_utils.PinholeCameraCfg( # focal_length=24.0, focus_distance=400.0, # horizontal_aperture=20.955, clipping_range=(0.1, 100.0), # ), # offset=CameraCfg.OffsetCfg(pos=CAM_POS, rot=CAM_ROT, convention="world"), # ) return ManagerBasedRLEnv(cfg) def init_output(output_dir: str) -> tuple[str, str]: """Create output directory, wipe any existing trajectory.hdf5, write JSON metadata.""" os.makedirs(output_dir, exist_ok=True) traj_path = os.path.join(output_dir, "trajectory.hdf5") json_path = os.path.join(output_dir, "trajectory.json") with h5py.File(traj_path, "w"): # truncate / create fresh pass with open(json_path, "w") as fh: json.dump({"env_info": {"env_kwargs": {"control_mode": "pd_joint_pos"}}}, fh) return traj_path, json_path def save_traj(traj_path: str, traj_idx: int, data: dict, save_images: bool) -> None: """Append one trajectory group to the consolidated HDF5.""" with h5py.File(traj_path, "a") as f: grp = f.create_group(f"traj_{traj_idx}") grp.create_dataset("obs", data=data["qpos"], compression="gzip") grp.create_dataset("actions", data=data["action"], compression="gzip") grp.create_dataset("qvel", data=data["qvel"], compression="gzip") grp.create_dataset("ee_pos", data=data["ee_pos"], compression="gzip") grp.create_dataset("ee_quat", data=data["ee_quat"], compression="gzip") if save_images and "frames" in data: grp.create_group("images").create_dataset( "rgb", data=data["frames"], compression="gzip" ) def main() -> None: pick_objects = list(dict.fromkeys(args_cli.pick_objects)) need_camera = args_cli.save_video or args_cli.save_images env = build_env(pick_objects, need_camera) dev = env.unwrapped.device camera = env.unwrapped.scene["video_cam"] if need_camera else None robot = env.unwrapped.scene.articulations["robot"] arm_ids, _ = robot.find_joints(ARM_JOINT_NAMES) gripper_ids, _ = robot.find_joints(GRIPPER_JOINT_NAMES) ik_ctrl = CartesianController( robot=robot, ee_body_name=EE_BODY_NAME, arm_joint_names=ARM_JOINT_NAMES, num_envs=1, device=dev, command_type="pose", lambda_val=0.05, max_joint_delta=0.2, ) default_jpos = robot.data.default_joint_pos.clone() video_dir = None imageio = None if args_cli.save_video: video_dir = args_cli.video_dir or os.path.join(args_cli.output_dir, "videos") os.makedirs(video_dir, exist_ok=True) import imageio as _io imageio = _io traj_path, _ = init_output(args_cli.output_dir) rng = np.random.default_rng() # unseeded → different positions every run n_ok = 0 attempt = 0 while n_ok < args_cli.num_demos: attempt += 1 if args_cli.max_attempts and attempt > args_cli.max_attempts: print(f"\n[WARN] Reached --max_attempts={args_cli.max_attempts}; collected {n_ok} demos.") break print(f"\n[INFO] Demo {n_ok + 1}/{args_cli.num_demos} (attempt {attempt})") data = collect_one_demo( env, robot, ik_ctrl, arm_ids, gripper_ids, pick_objects, dev, default_jpos=default_jpos, rng=rng, camera=camera, trace=args_cli.trace, abort_failed_lift=args_cli.abort_failed_lift, ) if data is None: print("[WARN] Early termination — skipping.") continue if args_cli.only_success and not check_objects_in_basket(env, pick_objects): print("[WARN] Objects not in basket — skipping (--only_success).") for line in basket_status_lines(env, pick_objects): print(f"[WARN] {line}") for line in _trace_lines(data, pick_objects): print(f"[TRACE] {line}") continue for line in _trace_lines(data, pick_objects): print(f"[TRACE] {line}") save_traj(traj_path, n_ok, data, args_cli.save_images) T = len(data["qpos"]) notes = [f"{T} steps"] if args_cli.save_video and "frames" in data: vp = os.path.join(video_dir, f"demo_{n_ok:04d}.mp4") imageio.mimwrite(vp, data["frames"], fps=50, quality=7) notes.append(f"video → {vp}") if args_cli.save_images and "frames" in data: notes.append("images saved") print(f"[INFO] traj_{n_ok}: {', '.join(notes)}") n_ok += 1 print(f"\n[INFO] Collected {n_ok} demos → {traj_path}") env.close() if __name__ == "__main__": main() simulation_app.close()