scripts/act/search_task_e_grasps.py

"""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()