"""Search per-object scripted grasp parameters for Task E. This is a fast physics-only diagnostic: it disables cameras, runs candidate grasp offsets/heights/yaw corrections in one Isaac process, and reports the first successful candidates. """ import argparse import math import os from isaaclab.app import AppLauncher parser = argparse.ArgumentParser(description="Search Task-E grasp candidates.") parser.add_argument("--objects", type=int, nargs="+", default=[1, 2]) parser.add_argument("--trials", type=int, default=1) parser.add_argument("--max_candidates", type=int, default=40) parser.add_argument("--start_candidate", type=int, default=1, help="1-based candidate index to start from; useful for resuming long searches.") parser.add_argument("--max_joint_delta", type=float, default=0.2, help="Per-step IK joint target limit. Lower values make contact pushes gentler.") AppLauncher.add_app_launcher_args(parser) args_cli = parser.parse_args() app_launcher = AppLauncher(args_cli) simulation_app = app_launcher.app import numpy as np import torch from isaaclab.actuators import ImplicitActuatorCfg from isaaclab.envs import ManagerBasedRLEnv from atec_rl_lab.tasks.task_e.env_cfg import TaskEEnvPiperCfg from atec_rl_lab.utils import CartesianController from task_e import config as grasp_cfg from task_e.collector import ( basket_status_lines, check_objects_in_basket, collect_one_demo, ) from task_e.config import ( ACT_DAMPING, ACT_EFFORT_LIMIT, ACT_STIFFNESS, ACT_VEL_LIMIT, ARM_JOINT_NAMES, EE_BODY_NAME, GRIPPER_JOINT_NAMES, STEPS, ) BASE_STATE_STEP_OVERRIDES = { obj_idx: steps.copy() for obj_idx, steps in grasp_cfg.OBJ_STATE_STEP_OVERRIDES.items() } def _candidate_dict( dx: float, dy: float, z: float, carry_z: float, target_x: float, target_y: float, transport_gripper: str, yaw: float, push_y: float | None = None, *, push_x: float = 0.0, push_z: float = 0.0, transport_steps: int | None = None, place_steps: int | None = None, open_steps: int | None = None, close_steps: int | None = None, lift_steps: int | None = None, close_z: float | None = None, finger_target_z: float | None = None, finger_max_z: float | None = None, mode: str = "pick", push_x_gain: float | None = None, push_max_x: float | None = None, push_min_behind: float | None = None, ) -> dict[str, float]: return { "mode": mode, "dx": dx, "dy": dy, "z": z, "carry_z": carry_z, "place_z": min(carry_z, grasp_cfg.TABLE_TOP_Z + 0.15), "target_x": target_x, "target_y": target_y, "transport_gripper": transport_gripper, "yaw": yaw, "push_x": push_x, "push_y": push_y, "push_z": push_z, "transport_steps": transport_steps, "place_steps": place_steps, "open_steps": open_steps, "close_steps": close_steps, "lift_steps": lift_steps, "close_z": close_z, "finger_target_z": finger_target_z, "finger_max_z": finger_max_z, "push_x_gain": push_x_gain, "push_max_x": push_max_x, "push_min_behind": push_min_behind, } def _normalize_candidate(item: tuple | dict) -> dict[str, float]: if isinstance(item, dict): cand = item.copy() cand.setdefault("mode", "pick") cand.setdefault("place_z", min(cand["carry_z"], grasp_cfg.TABLE_TOP_Z + 0.15)) cand.setdefault("push_x", 0.0) cand.setdefault("push_y", None) cand.setdefault("push_z", 0.0) cand.setdefault("transport_steps", None) cand.setdefault("place_steps", None) cand.setdefault("open_steps", None) cand.setdefault("close_steps", None) cand.setdefault("lift_steps", None) cand.setdefault("close_z", None) cand.setdefault("finger_target_z", None) cand.setdefault("finger_max_z", None) cand.setdefault("push_x_gain", None) cand.setdefault("push_max_x", None) cand.setdefault("push_min_behind", None) return cand if len(item) == 8: dx, dy, z, carry_z, target_x, target_y, transport_gripper, yaw = item return _candidate_dict(dx, dy, z, carry_z, target_x, target_y, transport_gripper, yaw) dx, dy, z, carry_z, target_x, target_y, transport_gripper, yaw, push_y = item return _candidate_dict(dx, dy, z, carry_z, target_x, target_y, transport_gripper, yaw, push_y) def build_env() -> ManagerBasedRLEnv: cfg = TaskEEnvPiperCfg() cfg.seed = 123 cfg.scene.num_envs = 1 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, ) return ManagerBasedRLEnv(cfg) def candidates_for(obj_idx: int) -> list[dict[str, float]]: priority = [] if obj_idx == 1: # Sugar box has an off-centre USD root. Try real top-down pinch # grasps around the measured visible bbox centre first; keep push-like # sweeps only as a fallback diagnostic. priority = [ _candidate_dict(0.025, 0.000, 0.072, grasp_cfg.CARRY_Z, 0.00, 0.000, "close", 0.0, close_z=0.020, finger_target_z=-0.025, finger_max_z=0.050, transport_steps=2200, place_steps=300, open_steps=80), _candidate_dict(0.025, 0.000, 0.072, grasp_cfg.CARRY_Z, 0.00, 0.000, "close", 0.0, close_z=0.015, finger_target_z=-0.030, finger_max_z=0.060, transport_steps=2200, place_steps=300, open_steps=80), _candidate_dict(0.025, 0.000, 0.072, grasp_cfg.CARRY_Z, 0.00, 0.000, "close", 0.0, close_z=0.025, finger_target_z=-0.020, finger_max_z=0.045, transport_steps=2200, place_steps=300, open_steps=80), _candidate_dict(0.015, 0.000, 0.072, grasp_cfg.CARRY_Z, 0.00, 0.000, "close", 0.0, close_z=0.020, finger_target_z=-0.025, finger_max_z=0.050, transport_steps=2200, place_steps=300, open_steps=80), _candidate_dict(0.035, 0.000, 0.072, grasp_cfg.CARRY_Z, 0.00, 0.000, "close", 0.0, close_z=0.020, finger_target_z=-0.025, finger_max_z=0.050, transport_steps=2200, place_steps=300, open_steps=80), _candidate_dict(0.025, 0.010, 0.072, grasp_cfg.CARRY_Z, 0.00, 0.000, "close", 0.0, close_z=0.020, finger_target_z=-0.025, finger_max_z=0.050, transport_steps=2200, place_steps=300, open_steps=80), _candidate_dict(0.025, -0.010, 0.072, grasp_cfg.CARRY_Z, 0.00, 0.000, "close", 0.0, close_z=0.020, finger_target_z=-0.025, finger_max_z=0.050, transport_steps=2200, place_steps=300, open_steps=80), _candidate_dict(0.040, 0.180, 0.000, grasp_cfg.TABLE_TOP_Z + 0.055, 0.00, 0.000, "close", 0.0, transport_steps=1450, place_steps=320, open_steps=80, mode="push", push_x_gain=0.55, push_max_x=0.12, push_min_behind=0.035), _candidate_dict(0.000, 0.180, 0.000, grasp_cfg.TABLE_TOP_Z + 0.055, 0.00, 0.000, "close", 0.0, transport_steps=1450, place_steps=320, open_steps=80, mode="push", push_x_gain=0.55, push_max_x=0.12, push_min_behind=0.035), _candidate_dict(-0.040, 0.180, 0.000, grasp_cfg.TABLE_TOP_Z + 0.055, 0.00, 0.000, "close", 0.0, transport_steps=1450, place_steps=320, open_steps=80, mode="push", push_x_gain=0.60, push_max_x=0.14, push_min_behind=0.035), _candidate_dict(0.040, 0.220, 0.000, grasp_cfg.TABLE_TOP_Z + 0.055, 0.00, 0.000, "close", 0.0, transport_steps=1550, place_steps=360, open_steps=80, mode="push", push_x_gain=0.55, push_max_x=0.12, push_min_behind=0.040), _candidate_dict(0.000, 0.220, 0.000, grasp_cfg.TABLE_TOP_Z + 0.055, 0.00, 0.000, "close", 0.0, transport_steps=1550, place_steps=360, open_steps=80, mode="push", push_x_gain=0.55, push_max_x=0.12, push_min_behind=0.040), # Push-slide primitive: approach from +Y and drive the object # centre into the basket. This tests the contact-rich route # before more top-down pinch candidates. _candidate_dict(0.040, 0.150, 0.000, grasp_cfg.TABLE_TOP_Z + 0.060, 0.00, 0.000, "close", 0.0, transport_steps=850, place_steps=140, open_steps=80, mode="push", push_x_gain=0.65, push_max_x=0.10), _candidate_dict(0.000, 0.150, 0.000, grasp_cfg.TABLE_TOP_Z + 0.060, 0.00, 0.000, "close", 0.0, transport_steps=850, place_steps=140, open_steps=80, mode="push", push_x_gain=0.65, push_max_x=0.10), _candidate_dict(-0.040, 0.150, 0.000, grasp_cfg.TABLE_TOP_Z + 0.060, 0.00, 0.000, "close", 0.0, transport_steps=850, place_steps=140, open_steps=80, mode="push", push_x_gain=0.70, push_max_x=0.12), _candidate_dict(0.040, 0.190, 0.000, grasp_cfg.TABLE_TOP_Z + 0.060, 0.00, 0.000, "close", 0.0, transport_steps=950, place_steps=140, open_steps=80, mode="push", push_x_gain=0.65, push_max_x=0.10), _candidate_dict(0.000, 0.190, 0.000, grasp_cfg.TABLE_TOP_Z + 0.060, 0.00, 0.000, "close", 0.0, transport_steps=950, place_steps=140, open_steps=80, mode="push", push_x_gain=0.65, push_max_x=0.10), _candidate_dict(0.040, 0.150, 0.000, grasp_cfg.TABLE_TOP_Z + 0.050, 0.00, 0.000, "open", 0.0, transport_steps=950, place_steps=140, open_steps=80, mode="push", push_x_gain=0.65, push_max_x=0.10), _candidate_dict(0.040, 0.150, 0.000, grasp_cfg.TABLE_TOP_Z + 0.070, 0.00, 0.000, "close", math.pi / 2, transport_steps=850, place_steps=140, open_steps=80, mode="push", push_x_gain=0.65, push_max_x=0.10), _candidate_dict(0.040, 0.150, 0.000, grasp_cfg.TABLE_TOP_Z + 0.070, 0.00, 0.000, "close", -math.pi / 2, transport_steps=850, place_steps=140, open_steps=80, mode="push", push_x_gain=0.65, push_max_x=0.10), # Focused timing sweep after candidates 025-034 proved real # contact/lift but missed the basket. Keep the end effector low, # hold contact longer, then release near the basket plane. _candidate_dict(0.040, 0.140, -0.020, grasp_cfg.TABLE_TOP_Z + 0.025, -0.17, 0.000, "close", 0.0, -0.160, transport_steps=1000, place_steps=30, open_steps=80), _candidate_dict(0.040, 0.140, -0.020, grasp_cfg.TABLE_TOP_Z + 0.025, -0.17, 0.000, "close", 0.0, -0.180, transport_steps=900, place_steps=30, open_steps=80), _candidate_dict(0.040, 0.140, -0.020, grasp_cfg.TABLE_TOP_Z + 0.025, -0.17, 0.000, "close", 0.0, -0.140, transport_steps=1200, place_steps=30, open_steps=80), _candidate_dict(0.040, 0.140, -0.020, grasp_cfg.TABLE_TOP_Z + 0.025, -0.17, 0.000, "close", 0.0, -0.200, transport_steps=750, place_steps=30, open_steps=80), _candidate_dict(0.040, 0.140, -0.020, grasp_cfg.TABLE_TOP_Z + 0.015, -0.17, 0.000, "close", 0.0, -0.180, transport_steps=900, place_steps=30, open_steps=80), _candidate_dict(0.040, 0.140, -0.020, grasp_cfg.TABLE_TOP_Z + 0.025, -0.17, 0.000, "open", 0.0, -0.180, transport_steps=1000, place_steps=30, open_steps=80), _candidate_dict(0.040, 0.140, -0.020, grasp_cfg.TABLE_TOP_Z + 0.025, -0.17, 0.000, "open", 0.0, -0.250, transport_steps=850, place_steps=30, open_steps=80), _candidate_dict(0.040, 0.140, -0.020, grasp_cfg.TABLE_TOP_Z + 0.025, -0.17, 0.000, "close", 0.0, -0.180, push_x=-0.040, transport_steps=900, place_steps=30, open_steps=80), _candidate_dict(0.040, 0.140, -0.020, grasp_cfg.TABLE_TOP_Z + 0.025, -0.17, 0.000, "close", 0.0, -0.180, push_x=0.040, transport_steps=900, place_steps=30, open_steps=80), _candidate_dict(0.040, 0.160, -0.020, grasp_cfg.TABLE_TOP_Z + 0.025, -0.17, 0.000, "close", 0.0, -0.180, transport_steps=900, place_steps=30, open_steps=80), (-0.205, 0.104, 0.025, grasp_cfg.TABLE_TOP_Z + 0.140, 0.00, -0.240, "close", 0.0), (-0.205, 0.104, 0.045, grasp_cfg.TABLE_TOP_Z + 0.160, 0.00, -0.240, "close", 0.0), (-0.205, 0.104, 0.065, grasp_cfg.TABLE_TOP_Z + 0.180, 0.00, -0.240, "close", 0.0), (-0.205, 0.104, 0.045, grasp_cfg.TABLE_TOP_Z + 0.160, 0.00, -0.240, "close", math.pi / 2), (-0.205, 0.104, 0.045, grasp_cfg.TABLE_TOP_Z + 0.160, 0.00, -0.240, "close", -math.pi / 2), (-0.180, 0.104, 0.045, grasp_cfg.TABLE_TOP_Z + 0.160, 0.00, -0.240, "close", 0.0), (-0.230, 0.104, 0.045, grasp_cfg.TABLE_TOP_Z + 0.160, 0.00, -0.240, "close", 0.0), (-0.205, 0.084, 0.045, grasp_cfg.TABLE_TOP_Z + 0.160, 0.00, -0.240, "close", 0.0), (-0.205, 0.124, 0.045, grasp_cfg.TABLE_TOP_Z + 0.160, 0.00, -0.240, "close", 0.0), (-0.180, 0.084, 0.045, grasp_cfg.TABLE_TOP_Z + 0.160, 0.00, -0.240, "close", 0.0), (-0.230, 0.124, 0.045, grasp_cfg.TABLE_TOP_Z + 0.160, 0.00, -0.240, "close", 0.0), (-0.205, 0.104, 0.085, grasp_cfg.TABLE_TOP_Z + 0.200, 0.00, -0.240, "close", 0.0), (0.040, 0.140, -0.020, grasp_cfg.TABLE_TOP_Z + 0.035, 0.00, -0.240, "close", 0.0, -0.100), (0.040, 0.140, -0.020, grasp_cfg.TABLE_TOP_Z + 0.035, 0.00, -0.240, "close", 0.0, -0.120), (0.040, 0.120, -0.020, grasp_cfg.TABLE_TOP_Z + 0.035, 0.00, -0.240, "close", 0.0, -0.100), (0.040, 0.160, -0.020, grasp_cfg.TABLE_TOP_Z + 0.035, 0.00, -0.240, "close", 0.0, -0.100), (0.040, 0.140, 0.000, grasp_cfg.TABLE_TOP_Z + 0.050, 0.00, -0.240, "close", 0.0, -0.100), (0.040, 0.140, -0.020, grasp_cfg.TABLE_TOP_Z + 0.035, 0.00, -0.240, "close", 0.0, -0.140), (0.040, 0.140, -0.020, grasp_cfg.TABLE_TOP_Z + 0.035, 0.00, -0.240, "close", 0.0, -0.180), (0.040, 0.140, -0.020, grasp_cfg.TABLE_TOP_Z + 0.035, 0.00, -0.240, "close", 0.0, -0.220), (0.040, 0.120, -0.020, grasp_cfg.TABLE_TOP_Z + 0.035, 0.00, -0.240, "close", 0.0, -0.160), (0.040, 0.160, -0.020, grasp_cfg.TABLE_TOP_Z + 0.035, 0.00, -0.240, "close", 0.0, -0.160), (0.020, 0.140, -0.020, grasp_cfg.TABLE_TOP_Z + 0.035, 0.00, -0.240, "close", 0.0, -0.160), (0.060, 0.140, -0.020, grasp_cfg.TABLE_TOP_Z + 0.035, 0.00, -0.240, "close", 0.0, -0.160), # Focused refinement around candidate 019: it made real contact # and lifted object_1, but released high and too far +X/+Y. # Lower the sweep height and bias release toward basket centre/left. (0.040, 0.140, -0.020, grasp_cfg.TABLE_TOP_Z + 0.025, -0.17, 0.000, "close", 0.0, -0.160), (0.040, 0.140, -0.020, grasp_cfg.TABLE_TOP_Z + 0.025, -0.17, 0.000, "close", 0.0, -0.180), (0.040, 0.140, -0.020, grasp_cfg.TABLE_TOP_Z + 0.025, -0.10, 0.000, "close", 0.0, -0.160), (0.040, 0.140, -0.020, grasp_cfg.TABLE_TOP_Z + 0.025, -0.10, 0.000, "close", 0.0, -0.180), (0.040, 0.160, -0.020, grasp_cfg.TABLE_TOP_Z + 0.025, -0.17, 0.000, "close", 0.0, -0.160), (0.040, 0.160, -0.020, grasp_cfg.TABLE_TOP_Z + 0.025, -0.17, 0.000, "close", 0.0, -0.180), (0.040, 0.140, -0.020, grasp_cfg.TABLE_TOP_Z + 0.025, -0.17, -0.100, "close", 0.0, -0.160), (0.040, 0.140, -0.020, grasp_cfg.TABLE_TOP_Z + 0.025, -0.17, -0.100, "close", 0.0, -0.180), (0.040, 0.140, -0.020, grasp_cfg.TABLE_TOP_Z + 0.035, -0.17, 0.000, "close", 0.0, -0.160), (0.040, 0.140, -0.020, grasp_cfg.TABLE_TOP_Z + 0.035, -0.17, 0.000, "close", 0.0, -0.180), (0.040, 0.140, 0.000, grasp_cfg.TABLE_TOP_Z + 0.050, 0.00, -0.240, "close", 0.0, -0.160), (0.040, 0.140, -0.020, grasp_cfg.TABLE_TOP_Z + 0.035, 0.00, -0.300, "close", 0.0, -0.160), (0.040, 0.140, -0.020, grasp_cfg.TABLE_TOP_Z + 0.035, 0.08, -0.240, "close", 0.0, -0.160), (0.040, 0.140, -0.020, grasp_cfg.TABLE_TOP_Z + 0.035, 0.00, -0.240, "close", 0.0, -0.060), (0.040, 0.140, -0.020, grasp_cfg.TABLE_TOP_Z + 0.035, 0.00, -0.240, "close", 0.0, -0.100), (0.040, 0.160, -0.020, grasp_cfg.TABLE_TOP_Z + 0.035, 0.00, -0.240, "close", 0.0, -0.060), (0.040, 0.160, -0.020, grasp_cfg.TABLE_TOP_Z + 0.035, 0.00, -0.240, "close", 0.0, -0.100), (0.020, 0.140, -0.020, grasp_cfg.TABLE_TOP_Z + 0.035, 0.00, -0.240, "close", 0.0, -0.080), (0.060, 0.140, -0.020, grasp_cfg.TABLE_TOP_Z + 0.035, 0.00, -0.240, "close", 0.0, -0.080), (0.040, 0.120, -0.020, grasp_cfg.TABLE_TOP_Z + 0.035, 0.00, -0.240, "close", 0.0, -0.080), (0.040, 0.180, -0.020, grasp_cfg.TABLE_TOP_Z + 0.035, 0.00, -0.240, "close", 0.0, -0.080), (0.040, 0.140, 0.000, grasp_cfg.TABLE_TOP_Z + 0.050, 0.00, -0.240, "close", 0.0, -0.080), (0.040, 0.140, -0.020, grasp_cfg.TABLE_TOP_Z + 0.035, 0.00, -0.300, "close", 0.0, -0.080), (0.040, 0.160, -0.020, grasp_cfg.TABLE_TOP_Z + 0.035, 0.00, -0.750, "close", 0.0), (0.040, 0.160, -0.020, grasp_cfg.TABLE_TOP_Z + 0.035, 0.08, -0.750, "close", 0.0), (0.040, 0.160, -0.020, grasp_cfg.TABLE_TOP_Z + 0.035, 0.00, -0.600, "close", 0.0), (0.040, 0.140, -0.020, grasp_cfg.TABLE_TOP_Z + 0.035, 0.00, -0.750, "close", 0.0), (0.040, 0.120, -0.020, grasp_cfg.TABLE_TOP_Z + 0.035, 0.00, -0.750, "close", 0.0), (0.060, 0.160, -0.020, grasp_cfg.TABLE_TOP_Z + 0.035, 0.00, -0.750, "close", 0.0), (0.020, 0.160, -0.020, grasp_cfg.TABLE_TOP_Z + 0.035, 0.00, -0.750, "close", 0.0), (0.040, 0.160, 0.000, grasp_cfg.TABLE_TOP_Z + 0.050, 0.00, -0.750, "close", 0.0), (0.040, 0.140, 0.000, grasp_cfg.TABLE_TOP_Z + 0.050, 0.00, -0.750, "close", 0.0), (0.040, 0.160, -0.020, grasp_cfg.TABLE_TOP_Z + 0.035, 0.00, -0.750, "open", 0.0), (-0.205, 0.104, 0.045, grasp_cfg.TABLE_TOP_Z + 0.120, 0.00, -0.240, "close", 0.0), (-0.205, 0.104, 0.045, grasp_cfg.TABLE_TOP_Z + 0.120, 0.00, -0.240, "close", math.pi / 2), (-0.205, 0.104, 0.045, grasp_cfg.TABLE_TOP_Z + 0.120, 0.00, -0.240, "close", -math.pi / 2), (-0.205, 0.084, 0.045, grasp_cfg.TABLE_TOP_Z + 0.120, 0.00, -0.240, "close", 0.0), (-0.180, 0.104, 0.045, grasp_cfg.TABLE_TOP_Z + 0.120, 0.00, -0.240, "close", 0.0), (-0.230, 0.104, 0.045, grasp_cfg.TABLE_TOP_Z + 0.120, 0.00, -0.240, "close", 0.0), (-0.205, 0.124, 0.045, grasp_cfg.TABLE_TOP_Z + 0.120, 0.00, -0.240, "close", 0.0), (-0.205, 0.104, 0.065, grasp_cfg.TABLE_TOP_Z + 0.140, 0.00, -0.240, "close", 0.0), (-0.205, 0.104, 0.025, grasp_cfg.TABLE_TOP_Z + 0.100, 0.00, -0.240, "close", 0.0), (-0.205, 0.104, 0.045, grasp_cfg.TABLE_TOP_Z + 0.180, 0.00, -0.240, "close", 0.0), (-0.205, 0.104, 0.045, grasp_cfg.TABLE_TOP_Z + 0.120, -0.10, -0.240, "close", 0.0), (-0.205, 0.104, 0.045, grasp_cfg.TABLE_TOP_Z + 0.120, 0.00, -0.300, "close", 0.0), (0.040, 0.200, -0.020, grasp_cfg.TABLE_TOP_Z + 0.035, -0.17, -0.60, "close", 0.0), (0.040, 0.200, -0.020, grasp_cfg.TABLE_TOP_Z + 0.035, -0.10, -0.60, "close", 0.0), (0.040, 0.160, -0.020, grasp_cfg.TABLE_TOP_Z + 0.035, -0.17, -0.60, "close", 0.0), (0.080, 0.200, -0.020, grasp_cfg.TABLE_TOP_Z + 0.035, -0.17, -0.60, "close", 0.0), (-0.040, 0.200, -0.020, grasp_cfg.TABLE_TOP_Z + 0.035, -0.17, -0.60, "close", 0.0), (-0.096, 0.200, -0.020, grasp_cfg.TABLE_TOP_Z + 0.035, -0.17, -0.60, "close", 0.0), (0.040, 0.240, -0.020, grasp_cfg.TABLE_TOP_Z + 0.035, -0.17, -0.60, "close", 0.0), (0.040, 0.200, 0.000, grasp_cfg.TABLE_TOP_Z + 0.035, -0.17, -0.60, "close", 0.0), (0.040, 0.200, -0.020, grasp_cfg.TABLE_TOP_Z + 0.050, -0.17, -0.60, "close", 0.0), (0.040, 0.200, -0.020, grasp_cfg.TABLE_TOP_Z + 0.035, -0.17, -0.45, "close", 0.0), (0.040, 0.200, -0.020, grasp_cfg.TABLE_TOP_Z + 0.035, -0.17, -0.75, "close", 0.0), (0.040, 0.200, -0.020, grasp_cfg.TABLE_TOP_Z + 0.035, -0.17, -0.60, "open", 0.0), ] base_offsets = [ (0.040, 0.140), (0.040, 0.180), (0.040, 0.220), (0.080, 0.180), (0.080, 0.220), (-0.096, 0.140), (-0.096, 0.180), (-0.096, 0.220), (-0.040, 0.140), (-0.040, 0.180), (-0.040, 0.220), (0.000, 0.140), (0.000, 0.180), (0.000, 0.220), (-0.160, 0.140), (-0.200, 0.140), (-0.117, -0.025), (-0.096, 0.084), (-0.096, 0.000), (-0.060, 0.060), (-0.040, 0.020), (0.000, 0.040), (-0.140, 0.040), (-0.100, 0.000), (-0.020, 0.080), ] z_values = [-0.020, -0.010, 0.000, 0.015, 0.025, 0.040, 0.060, 0.080, 0.110] carry_values = [ grasp_cfg.TABLE_TOP_Z + 0.025, grasp_cfg.TABLE_TOP_Z + 0.035, grasp_cfg.TABLE_TOP_Z + 0.050, grasp_cfg.TABLE_TOP_Z + 0.075, grasp_cfg.TABLE_TOP_Z + 0.10, grasp_cfg.TABLE_TOP_Z + 0.12, grasp_cfg.TABLE_TOP_Z + 0.16, grasp_cfg.TABLE_TOP_Z + 0.22, grasp_cfg.CARRY_Z, ] target_x_offsets = [-0.17, -0.10, 0.0, 0.08] target_y_offsets = [-0.60, -0.45, -0.75, -0.36, -0.30, -0.24, -0.18, 0.0] transport_gripper_cmds = ["close", "open"] yaws = [0.0, math.pi / 2, -math.pi / 2] elif obj_idx == 2: priority = [ _candidate_dict(0.000, 0.045, 0.000, grasp_cfg.TABLE_TOP_Z + 0.045, 0.00, -0.140, "close", 0.0, transport_steps=1500, place_steps=420, open_steps=80, mode="push", push_x_gain=0.50, push_max_x=0.10, push_min_behind=-0.015), _candidate_dict(0.020, 0.045, 0.000, grasp_cfg.TABLE_TOP_Z + 0.045, 0.00, -0.140, "close", 0.0, transport_steps=1500, place_steps=420, open_steps=80, mode="push", push_x_gain=0.50, push_max_x=0.10, push_min_behind=-0.015), _candidate_dict(-0.020, 0.045, 0.000, grasp_cfg.TABLE_TOP_Z + 0.045, 0.00, -0.140, "close", 0.0, transport_steps=1500, place_steps=420, open_steps=80, mode="push", push_x_gain=0.55, push_max_x=0.12, push_min_behind=-0.015), _candidate_dict(0.000, 0.060, 0.000, grasp_cfg.TABLE_TOP_Z + 0.040, 0.00, -0.180, "close", 0.0, transport_steps=1600, place_steps=460, open_steps=80, mode="push", push_x_gain=0.50, push_max_x=0.10, push_min_behind=-0.020), _candidate_dict(0.020, 0.060, 0.000, grasp_cfg.TABLE_TOP_Z + 0.040, 0.00, -0.180, "close", 0.0, transport_steps=1600, place_steps=460, open_steps=80, mode="push", push_x_gain=0.50, push_max_x=0.10, push_min_behind=-0.020), _candidate_dict(-0.020, 0.060, 0.000, grasp_cfg.TABLE_TOP_Z + 0.040, 0.00, -0.180, "close", 0.0, transport_steps=1600, place_steps=460, open_steps=80, mode="push", push_x_gain=0.55, push_max_x=0.12, push_min_behind=-0.020), _candidate_dict(0.000, 0.095, 0.000, grasp_cfg.TABLE_TOP_Z + 0.045, 0.00, 0.000, "close", 0.0, transport_steps=1250, place_steps=260, open_steps=80, mode="push", push_x_gain=0.55, push_max_x=0.10, push_min_behind=0.025), _candidate_dict(0.020, 0.095, 0.000, grasp_cfg.TABLE_TOP_Z + 0.045, 0.00, 0.000, "close", 0.0, transport_steps=1250, place_steps=260, open_steps=80, mode="push", push_x_gain=0.55, push_max_x=0.10, push_min_behind=0.025), _candidate_dict(-0.020, 0.095, 0.000, grasp_cfg.TABLE_TOP_Z + 0.045, 0.00, 0.000, "close", 0.0, transport_steps=1250, place_steps=260, open_steps=80, mode="push", push_x_gain=0.60, push_max_x=0.12, push_min_behind=0.025), _candidate_dict(0.000, 0.125, 0.000, grasp_cfg.TABLE_TOP_Z + 0.045, 0.00, 0.000, "close", 0.0, transport_steps=1350, place_steps=300, open_steps=80, mode="push", push_x_gain=0.55, push_max_x=0.10, push_min_behind=0.030), _candidate_dict(0.020, 0.125, 0.000, grasp_cfg.TABLE_TOP_Z + 0.045, 0.00, 0.000, "close", 0.0, transport_steps=1350, place_steps=300, open_steps=80, mode="push", push_x_gain=0.55, push_max_x=0.10, push_min_behind=0.030), _candidate_dict(0.000, 0.095, 0.000, grasp_cfg.TABLE_TOP_Z + 0.040, 0.00, 0.000, "close", 0.0, transport_steps=1350, place_steps=300, open_steps=80, mode="push", push_x_gain=0.55, push_max_x=0.10, push_min_behind=0.020), _candidate_dict(-0.040, 0.070, 0.000, grasp_cfg.TABLE_TOP_Z + 0.045, 0.00, -0.140, "close", 0.0, transport_steps=1000, place_steps=160, open_steps=80, mode="push", push_x_gain=0.75, push_max_x=0.14), _candidate_dict(0.000, 0.070, 0.000, grasp_cfg.TABLE_TOP_Z + 0.045, 0.00, -0.140, "close", 0.0, transport_steps=1000, place_steps=160, open_steps=80, mode="push", push_x_gain=0.70, push_max_x=0.12), _candidate_dict(0.040, 0.070, 0.000, grasp_cfg.TABLE_TOP_Z + 0.045, 0.00, -0.140, "close", 0.0, transport_steps=1000, place_steps=160, open_steps=80, mode="push", push_x_gain=0.70, push_max_x=0.12), _candidate_dict(-0.040, 0.050, 0.000, grasp_cfg.TABLE_TOP_Z + 0.040, 0.00, -0.180, "close", 0.0, transport_steps=1100, place_steps=180, open_steps=80, mode="push", push_x_gain=0.75, push_max_x=0.14), _candidate_dict(0.000, 0.050, 0.000, grasp_cfg.TABLE_TOP_Z + 0.040, 0.00, -0.180, "close", 0.0, transport_steps=1100, place_steps=180, open_steps=80, mode="push", push_x_gain=0.70, push_max_x=0.12), _candidate_dict(-0.040, 0.090, 0.000, grasp_cfg.TABLE_TOP_Z + 0.050, 0.00, -0.220, "open", 0.0, transport_steps=1100, place_steps=180, open_steps=80, mode="push", push_x_gain=0.75, push_max_x=0.14), _candidate_dict(0.000, 0.115, 0.000, grasp_cfg.TABLE_TOP_Z + 0.065, 0.00, 0.000, "close", 0.0, transport_steps=700, place_steps=120, open_steps=80, mode="push", push_x_gain=0.65, push_max_x=0.10), _candidate_dict(0.040, 0.115, 0.000, grasp_cfg.TABLE_TOP_Z + 0.065, 0.00, 0.000, "close", 0.0, transport_steps=700, place_steps=120, open_steps=80, mode="push", push_x_gain=0.65, push_max_x=0.10), _candidate_dict(-0.040, 0.115, 0.000, grasp_cfg.TABLE_TOP_Z + 0.065, 0.00, 0.000, "close", 0.0, transport_steps=700, place_steps=120, open_steps=80, mode="push", push_x_gain=0.70, push_max_x=0.12), _candidate_dict(0.000, 0.150, 0.000, grasp_cfg.TABLE_TOP_Z + 0.065, 0.00, 0.000, "close", 0.0, transport_steps=850, place_steps=120, open_steps=80, mode="push", push_x_gain=0.65, push_max_x=0.10), _candidate_dict(0.040, 0.150, 0.000, grasp_cfg.TABLE_TOP_Z + 0.065, 0.00, 0.000, "close", -math.pi / 2, transport_steps=850, place_steps=120, open_steps=80, mode="push", push_x_gain=0.65, push_max_x=0.10), _candidate_dict(0.000, 0.115, 0.000, grasp_cfg.TABLE_TOP_Z + 0.055, 0.00, 0.000, "open", 0.0, transport_steps=850, place_steps=120, open_steps=80, mode="push", push_x_gain=0.65, push_max_x=0.10), (0.006, 0.046, 0.045, grasp_cfg.TABLE_TOP_Z + 0.075, 0.00, -0.240, "close", -math.pi / 2), ] base_offsets = [ (0.006, 0.046), (0.030, 0.026), (0.060, 0.000), (0.100, 0.020), (0.115, 0.026), (-0.020, 0.050), (0.040, 0.070), (0.000, 0.000), ] z_values = [0.045, 0.065, 0.085, 0.110, 0.135, 0.160] carry_values = [ grasp_cfg.TABLE_TOP_Z + 0.075, grasp_cfg.TABLE_TOP_Z + 0.10, grasp_cfg.TABLE_TOP_Z + 0.12, grasp_cfg.TABLE_TOP_Z + 0.14, grasp_cfg.TABLE_TOP_Z + 0.18, grasp_cfg.TABLE_TOP_Z + 0.24, grasp_cfg.CARRY_Z, ] target_x_offsets = [0.0] target_y_offsets = [-0.24, -0.18, -0.30, -0.12, 0.0] transport_gripper_cmds = ["close"] yaws = [0.0, math.pi / 2, -math.pi / 2] else: base_offsets = [(0.0, 0.0)] z_values = [grasp_cfg.GRASP_Z_OFFSET] carry_values = [grasp_cfg.CARRY_Z] target_x_offsets = [0.0] target_y_offsets = [0.0] transport_gripper_cmds = ["close"] yaws = [0.0] out = [] for item in priority: out.append(_normalize_candidate(item)) for z in z_values: for carry_z in carry_values: for target_x in target_x_offsets: for target_y in target_y_offsets: for transport_gripper in transport_gripper_cmds: for yaw in yaws: for dx, dy in base_offsets: out.append({ "dx": dx, "dy": dy, "z": z, "carry_z": carry_z, "place_z": min(carry_z, grasp_cfg.TABLE_TOP_Z + 0.15), "target_x": target_x, "target_y": target_y, "transport_gripper": transport_gripper, "yaw": yaw, "push_y": None, }) start = max(args_cli.start_candidate, 1) - 1 return out[start : start + args_cli.max_candidates] def apply_candidate(obj_idx: int, cand: dict[str, float]) -> None: grasp_cfg.OBJ_MANIPULATION_MODES[obj_idx] = cand.get("mode", "pick") grasp_cfg.OBJ_GRASP_CENTER_OFFSETS[obj_idx] = (cand["dx"], cand["dy"], 0.0) grasp_cfg.OBJ_GRASP_Z_OFFSETS[obj_idx] = cand["z"] if cand.get("close_z") is not None: grasp_cfg.OBJ_CLOSE_Z_OFFSETS[obj_idx] = float(cand["close_z"]) grasp_cfg.OBJ_GRASP_YAW_OFFSETS[obj_idx] = cand["yaw"] grasp_cfg.OBJ_CARRY_Z[obj_idx] = cand["carry_z"] grasp_cfg.OBJ_PLACE_HEIGHTS[obj_idx] = cand["place_z"] grasp_cfg.OBJ_PLACE_XY_OFFSETS[obj_idx] = (cand["target_x"], cand["target_y"]) grasp_cfg.OBJ_TRANSPORT_GRIPPER_CMDS[obj_idx] = cand["transport_gripper"] grasp_cfg.OBJ_PUSH_GRIPPER_CMDS[obj_idx] = cand["transport_gripper"] if cand.get("finger_target_z") is not None: grasp_cfg.OBJ_FINGER_CENTER_SERVO_TARGET_Z[obj_idx] = float(cand["finger_target_z"]) if cand.get("finger_max_z") is not None: grasp_cfg.OBJ_FINGER_CENTER_SERVO_MAX_Z[obj_idx] = float(cand["finger_max_z"]) if cand.get("push_x_gain") is not None: grasp_cfg.OBJ_PUSH_X_GAINS[obj_idx] = float(cand["push_x_gain"]) if cand.get("push_max_x") is not None: grasp_cfg.OBJ_PUSH_MAX_X_CORRECTIONS[obj_idx] = float(cand["push_max_x"]) if cand.get("push_min_behind") is not None: grasp_cfg.OBJ_PUSH_MIN_BEHIND[obj_idx] = float(cand["push_min_behind"]) grasp_cfg.OBJ_TRANSPORT_PUSH_BIASES[obj_idx] = ( None if cand.get("push_y") is None else (cand.get("push_x", 0.0), cand["push_y"], cand.get("push_z", 0.0)) ) step_overrides = BASE_STATE_STEP_OVERRIDES.get(obj_idx, {}).copy() for cand_key, state_key in ( ("close_steps", "CLOSE"), ("lift_steps", "LIFT"), ("transport_steps", "TRANSPORT"), ("place_steps", "PLACE"), ("open_steps", "OPEN"), ): if cand.get(cand_key) is not None: step_overrides[state_key] = int(cand[cand_key]) if step_overrides: grasp_cfg.OBJ_STATE_STEP_OVERRIDES[obj_idx] = step_overrides else: grasp_cfg.OBJ_STATE_STEP_OVERRIDES.pop(obj_idx, None) def format_trace(data: dict | None, obj_idx: int) -> str: if data is None or "trace" not in data: return "trace=none" tr = data["trace"].get(f"object_{obj_idx}", {}) states = tr.get("states", {}) close = states.get("CLOSE", {}) lift = states.get("LIFT", {}) transport = states.get("TRANSPORT", {}) def _fmt_vec(vec): if vec is None: return "none" return "(" + ",".join(f"{float(v):+.3f}" for v in vec[:3]) + ")" return ( f"lifted={tr.get('lifted')} " f"reward_lifted={tr.get('reward_lifted')} " f"z_gain={float(tr.get('z_gain', 0.0)):.3f} " f"gap_close={float(close.get('min_gripper_gap', 999.0)):.3f} " f"gap_lift={float(lift.get('min_gripper_gap', 999.0)):.3f} " f"ee_lift={float(lift.get('min_ee_dist', 999.0)):.3f} " f"ee_transport={float(transport.get('min_ee_dist', 999.0)):.3f} " f"finger_lift={float(lift.get('min_finger_center_dist', 999.0)):.3f} " f"finger_gap={float(lift.get('min_finger_body_gap', 999.0)):.3f} " f"finger_vec_transport={_fmt_vec(transport.get('min_finger_center_vec'))} " f"ee_vec_transport={_fmt_vec(transport.get('min_ee_vec'))}" ) def score_candidate(env: ManagerBasedRLEnv, data: dict | None, obj_idx: int, ok: bool) -> float: pos = env.unwrapped.scene.rigid_objects[f"object_{obj_idx}"].data.root_pos_w[0] dx = abs(float(pos[0].item() - grasp_cfg.BASKET_CENTER_X)) dy = abs(float(pos[1].item() - grasp_cfg.BASKET_CENTER_Y)) xy_err = max(dx - grasp_cfg.BASKET_IN_X, 0.0) + max(dy - grasp_cfg.BASKET_IN_Y, 0.0) z_gain = 0.0 if data is not None and "trace" in data: z_gain = float(data["trace"].get(f"object_{obj_idx}", {}).get("z_gain", 0.0)) return (100.0 if ok else 0.0) + 2.0 * z_gain - xy_err def main() -> None: env = build_env() dev = env.unwrapped.device 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=args_cli.max_joint_delta, ) default_jpos = robot.data.default_joint_pos.clone() original_close = STEPS["CLOSE"] STEPS["CLOSE"] = max(original_close, 100) try: for obj_idx in args_cli.objects: print(f"\n[SEARCH] object_{obj_idx}") successes: list[dict[str, float]] = [] best: tuple[float, int, dict[str, float], str] | None = None for cand_idx, cand in enumerate(candidates_for(obj_idx), start=max(args_cli.start_candidate, 1)): apply_candidate(obj_idx, cand) ok_count = 0 last_status = "" last_score = -1e9 for trial in range(args_cli.trials): # Keep trial seeds independent of candidate index so every # candidate is evaluated on the same object placements. rng = np.random.default_rng(1000 + obj_idx * 100 + trial) data = collect_one_demo( env, robot, ik_ctrl, arm_ids, gripper_ids, [obj_idx], dev, default_jpos=default_jpos, rng=rng, camera=None, trace=True, ) ok = data is not None and check_objects_in_basket(env, [obj_idx]) ok_count += int(ok) last_score = score_candidate(env, data, obj_idx, ok) last_status = ( "; ".join(basket_status_lines(env, [obj_idx])) + " | " + format_trace(data, obj_idx) ) if not ok: break if best is None or last_score > best[0]: best = (last_score, cand_idx, cand.copy(), last_status) result = f"{ok_count}/{args_cli.trials}" print( f"[CAND {cand_idx:03d}] mode={cand.get('mode', 'pick')} result={result} " f"dx={cand['dx']:+.3f} dy={cand['dy']:+.3f} " f"z={cand['z']:.3f} close_z={cand.get('close_z')} " f"finger_z={cand.get('finger_target_z')} carry={cand['carry_z']:.3f} " f"target_x={cand['target_x']:+.3f} " f"target_y={cand['target_y']:+.3f} " f"push=({cand.get('push_x', 0.0):+.3f},{cand.get('push_y')},{cand.get('push_z', 0.0):+.3f}) " f"behind={cand.get('push_min_behind')} " f"steps=({cand.get('transport_steps')},{cand.get('place_steps')},{cand.get('open_steps')}) " f"transport_gripper={cand['transport_gripper']} " f"yaw={cand['yaw']:+.3f} | {last_status}", flush=True, ) if ok_count == args_cli.trials: successes.append(cand.copy()) print(f"[SUCCESS] object_{obj_idx}: {cand}", flush=True) break if not successes: print(f"[FAIL] object_{obj_idx}: no successful candidate in first {args_cli.max_candidates}") if best is not None: best_score, best_idx, best_cand, best_status = best print( f"[BEST] object_{obj_idx}: cand={best_idx:03d} score={best_score:.3f} " f"{best_cand} | {best_status}", flush=True, ) finally: STEPS["CLOSE"] = original_close env.close() if __name__ == "__main__": main() simulation_app.close()