613 lines
24 KiB
Python
613 lines
24 KiB
Python
#
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# Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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#
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# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
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# property and proprietary rights in and to this material, related
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# documentation and any modifications thereto. Any use, reproduction,
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# disclosure or distribution of this material and related documentation
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# without an express license agreement from NVIDIA CORPORATION or
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# its affiliates is strictly prohibited.
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#
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# Standard Library
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import argparse
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import time
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from copy import deepcopy
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from typing import Any, Dict, List, Optional
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# Third Party
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import numpy as np
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import torch
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from geometrout.primitive import Cuboid, Cylinder
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from geometrout.transform import SE3
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from metrics import CuroboGroupMetrics, CuroboMetrics
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from robometrics.datasets import demo_raw, motion_benchmaker_raw, mpinets_raw
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from robometrics.evaluator import Evaluator
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from robometrics.robot import CollisionSphereConfig, Robot
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from tqdm import tqdm
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# CuRobo
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from curobo.geom.sdf.world import CollisionCheckerType, WorldConfig
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from curobo.types.base import TensorDeviceType
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from curobo.types.math import Pose
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from curobo.types.robot import RobotConfig
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from curobo.types.state import JointState
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from curobo.util.logger import setup_curobo_logger
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from curobo.util_file import (
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get_assets_path,
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get_robot_configs_path,
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get_task_configs_path,
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get_world_configs_path,
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join_path,
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load_yaml,
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write_yaml,
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)
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from curobo.wrap.reacher.motion_gen import MotionGen, MotionGenConfig, MotionGenPlanConfig
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torch.manual_seed(0)
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torch.backends.cudnn.benchmark = True
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torch.backends.cuda.matmul.allow_tf32 = True
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torch.backends.cudnn.allow_tf32 = True
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np.random.seed(0)
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def load_curobo(
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n_cubes: int,
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enable_debug: bool = False,
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tsteps: int = 30,
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trajopt_seeds: int = 4,
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mpinets: bool = False,
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graph_mode: bool = True,
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):
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robot_cfg = load_yaml(join_path(get_robot_configs_path(), "franka.yml"))["robot_cfg"]
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robot_cfg["kinematics"]["collision_sphere_buffer"] = 0.0
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robot_cfg["kinematics"]["collision_spheres"] = "spheres/franka_mesh.yml"
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robot_cfg["kinematics"]["collision_link_names"].remove("attached_object")
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ik_seeds = 30 # 500
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if graph_mode:
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trajopt_seeds = 4
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if trajopt_seeds >= 14:
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ik_seeds = max(100, trajopt_seeds * 4)
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if mpinets:
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robot_cfg["kinematics"]["lock_joints"] = {
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"panda_finger_joint1": 0.025,
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"panda_finger_joint2": -0.025,
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}
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world_cfg = WorldConfig.from_dict(
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load_yaml(join_path(get_world_configs_path(), "collision_table.yml"))
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).get_obb_world()
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interpolation_steps = 2000
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if graph_mode:
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interpolation_steps = 100
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robot_cfg_instance = RobotConfig.from_dict(robot_cfg, tensor_args=TensorDeviceType())
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K = robot_cfg_instance.kinematics.kinematics_config.joint_limits
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K.position[0, :] -= 0.1
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K.position[1, :] += 0.1
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motion_gen_config = MotionGenConfig.load_from_robot_config(
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robot_cfg_instance,
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world_cfg,
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trajopt_tsteps=tsteps,
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collision_checker_type=CollisionCheckerType.PRIMITIVE,
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use_cuda_graph=True,
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collision_cache={"obb": n_cubes},
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position_threshold=0.005, # 0.5 cm
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rotation_threshold=0.05,
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num_ik_seeds=ik_seeds,
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num_graph_seeds=trajopt_seeds,
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num_trajopt_seeds=trajopt_seeds,
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interpolation_dt=0.025,
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interpolation_steps=interpolation_steps,
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collision_activation_distance=0.03,
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state_finite_difference_mode="CENTRAL",
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trajopt_dt=0.25,
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minimize_jerk=True,
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finetune_dt_scale=1.05, # 1.05,
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maximum_trajectory_dt=0.1,
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)
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mg = MotionGen(motion_gen_config)
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mg.warmup(enable_graph=True, warmup_js_trajopt=False)
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robot_cfg["kinematics"]["ee_link"] = "panda_hand"
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return mg, robot_cfg
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def benchmark_mb(
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write_usd=False,
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save_log=False,
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write_plot=False,
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write_benchmark=False,
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plot_cost=False,
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override_tsteps: Optional[int] = None,
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save_kpi=False,
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graph_mode=False,
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args=None,
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):
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interpolation_dt = 0.02
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enable_debug = False
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robot_cfg = load_yaml(join_path(get_robot_configs_path(), "franka.yml"))["robot_cfg"]
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urdf = join_path(get_assets_path(), robot_cfg["kinematics"]["urdf_path"])
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spheres = robot_cfg["kinematics"]["collision_spheres"]
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if isinstance(spheres, str):
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spheres = load_yaml(join_path(get_robot_configs_path(), spheres))["collision_spheres"]
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for s in spheres:
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for k in spheres[s]:
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k["radius"] = max(0.001, k["radius"] - 0.015)
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data = {
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"collision_spheres": spheres,
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"self_collision_ignore": robot_cfg["kinematics"]["self_collision_ignore"],
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"self_collision_buffer": robot_cfg["kinematics"]["self_collision_buffer"],
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}
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config = CollisionSphereConfig.load_from_dictionary(data)
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metrics_robot = Robot(urdf, config)
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evaluator = Evaluator(metrics_robot)
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all_files = []
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# modify robot joint limits as some start states in the dataset are at the joint limits:
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if True:
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for k in evaluator.robot.actuated_joints:
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k.limit.lower -= 0.1
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k.limit.upper += 0.1
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plan_config = MotionGenPlanConfig(
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max_attempts=60,
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enable_graph_attempt=3,
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enable_finetune_trajopt=False,
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partial_ik_opt=True,
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)
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file_paths = [motion_benchmaker_raw, mpinets_raw]
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if args.demo:
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file_paths = [demo_raw]
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# load dataset:
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og_tsteps = 32
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if override_tsteps is not None:
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og_tsteps = override_tsteps
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og_trajopt_seeds = 12
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for file_path in file_paths:
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all_groups = []
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mpinets_data = False
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problems = file_path()
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if "dresser_task_oriented" in list(problems.keys()):
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mpinets_data = True
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for key, v in tqdm(problems.items()):
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tsteps = og_tsteps
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trajopt_seeds = og_trajopt_seeds
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if "cage_panda" in key:
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trajopt_seeds = 16
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if "table_under_pick_panda" in key:
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tsteps = 44
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trajopt_seeds = 28
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if "cubby_task_oriented" in key and "merged" not in key:
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trajopt_seeds = 16
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if "dresser_task_oriented" in key:
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trajopt_seeds = 16
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scene_problems = problems[key] # [:4] # [:1] # [:20] # [0:10]
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n_cubes = check_problems(scene_problems)
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mg, robot_cfg = load_curobo(
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n_cubes, enable_debug, tsteps, trajopt_seeds, mpinets_data, graph_mode
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)
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m_list = []
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i = 0
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ik_fail = 0
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for problem in tqdm(scene_problems, leave=False):
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i += 1
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if problem["collision_buffer_ik"] < 0.0:
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continue
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plan_config = MotionGenPlanConfig(
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max_attempts=100, # 00, # 00, # 100, # 00, # 000,#,00,#00, # 5000,
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enable_graph_attempt=3,
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enable_finetune_trajopt=True,
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partial_ik_opt=False,
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enable_graph=graph_mode,
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timeout=60,
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enable_opt=not graph_mode,
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)
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q_start = problem["start"]
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pose = (
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problem["goal_pose"]["position_xyz"] + problem["goal_pose"]["quaternion_wxyz"]
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)
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problem_name = "d_" + key + "_" + str(i)
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# reset planner
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mg.reset(reset_seed=False)
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world = WorldConfig.from_dict(deepcopy(problem["obstacles"])).get_obb_world()
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# world.save_world_as_mesh(problem_name + ".stl")
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mg.world_coll_checker.clear_cache()
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mg.update_world(world)
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# continue
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# load obstacles
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# run planner
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start_state = JointState.from_position(mg.tensor_args.to_device([q_start]))
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result = mg.plan_single(
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start_state,
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Pose.from_list(pose),
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plan_config,
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)
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if result.status == "IK Fail":
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ik_fail += 1
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if result.success.item():
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eval_obs = get_metrics_obstacles(problem["obstacles"])
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q_traj = result.get_interpolated_plan()
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ee_trajectory = mg.compute_kinematics(q_traj)
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q_metrics = q_traj.position.cpu().numpy()
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ee_m = ee_trajectory.ee_pose
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ee_pos = ee_m.position.cpu().numpy()
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ee_q = ee_m.quaternion.cpu().numpy()
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se3_list = [
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SE3(np.ravel(ee_pos[p]), np.ravel(ee_q[p]))
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for p in range(ee_m.position.shape[0])
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]
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# add gripper position:
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q_met = np.zeros((q_metrics.shape[0], 8))
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q_met[:, :7] = q_metrics
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q_met[:, 7] = 0.04
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if mpinets_data:
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q_met[:, 7] = 0.025
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st_time = time.time()
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current_metrics = evaluator.evaluate_trajectory(
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q_met,
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se3_list,
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SE3(np.ravel(pose[:3]), np.ravel(pose[3:])),
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eval_obs,
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result.total_time,
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)
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# if not current_metrics.success:
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# print(current_metrics)
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# write_usd = True
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# else:
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# write_usd = False
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# rint(plan_config.enable_graph, plan_config.enable_graph_attempt)
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problem["solution"] = None
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if plan_config.enable_finetune_trajopt:
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problem_name = key + "_" + str(i)
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else:
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problem_name = "noft_" + key + "_" + str(i)
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problem_name = "nosw_" + problem_name
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if write_usd or save_log:
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# CuRobo
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from curobo.util.usd_helper import UsdHelper
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world.randomize_color(r=[0.5, 0.9], g=[0.2, 0.5], b=[0.0, 0.2])
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gripper_mesh = Mesh(
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name="target_gripper",
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file_path=join_path(
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get_assets_path(),
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"robot/franka_description/meshes/visual/hand_ee_link.dae",
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),
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color=[0.0, 0.8, 0.1, 1.0],
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pose=pose,
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)
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world.add_obstacle(gripper_mesh)
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# get costs:
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if plot_cost:
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dt = 0.5
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problem_name = "approx_wolfe_p" + problem_name
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if result.optimized_dt is not None:
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dt = result.optimized_dt.item()
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if "trajopt_result" in result.debug_info:
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success = result.success.item()
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traj_cost = (
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# result.debug_info["trajopt_result"].debug_info["solver"]["cost"][0]
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result.debug_info["trajopt_result"].debug_info["solver"]["cost"][-1]
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)
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# print(traj_cost[0])
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traj_cost = torch.cat(traj_cost, dim=-1)
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plot_cost_iteration(
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traj_cost,
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title=problem_name + "_" + str(success) + "_" + str(dt),
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save_path=join_path("log/plot/", problem_name + "_cost"),
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log_scale=False,
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)
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if "finetune_trajopt_result" in result.debug_info:
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traj_cost = result.debug_info["finetune_trajopt_result"].debug_info[
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"solver"
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]["cost"][0]
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traj_cost = torch.cat(traj_cost, dim=-1)
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plot_cost_iteration(
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traj_cost,
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title=problem_name + "_" + str(success) + "_" + str(dt),
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save_path=join_path("log/plot/", problem_name + "_ft_cost"),
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log_scale=False,
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)
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if result.success.item() and current_metrics.success:
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q_traj = result.get_interpolated_plan()
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problem["goal_ik"] = q_traj.position.cpu().squeeze().numpy()[-1, :].tolist()
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problem["solution"] = {
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"position": result.get_interpolated_plan()
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.position.cpu()
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.squeeze()
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.numpy()
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.tolist(),
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"velocity": result.get_interpolated_plan()
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.velocity.cpu()
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.squeeze()
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.numpy()
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.tolist(),
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"acceleration": result.get_interpolated_plan()
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.acceleration.cpu()
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.squeeze()
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.numpy()
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.tolist(),
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"jerk": result.get_interpolated_plan()
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.jerk.cpu()
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.squeeze()
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.numpy()
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.tolist(),
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"dt": interpolation_dt,
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}
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# print(problem["solution"]["position"])
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# exit()
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debug = {
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"used_graph": result.used_graph,
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"attempts": result.attempts,
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"ik_time": result.ik_time,
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"graph_time": result.graph_time,
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"trajopt_time": result.trajopt_time,
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"total_time": result.total_time,
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"solve_time": result.solve_time,
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"opt_traj": {
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"position": result.optimized_plan.position.cpu()
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.squeeze()
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.numpy()
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.tolist(),
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"velocity": result.optimized_plan.velocity.cpu()
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.squeeze()
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.numpy()
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.tolist(),
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"acceleration": result.optimized_plan.acceleration.cpu()
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.squeeze()
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.numpy()
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.tolist(),
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"jerk": result.optimized_plan.jerk.cpu().squeeze().numpy().tolist(),
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"dt": result.optimized_dt.item(),
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},
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"valid_query": result.valid_query,
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}
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problem["solution_debug"] = debug
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# print(
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# "T: ",
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# result.motion_time.item(),
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# result.optimized_dt.item(),
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# (len(problem["solution"]["position"]) - 1 ) * result.interpolation_dt,
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# result.interpolation_dt,
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# )
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# exit()
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current_metrics = CuroboMetrics(
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skip=False,
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success=True,
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time=result.total_time,
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collision=False,
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joint_limit_violation=False,
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self_collision=False,
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position_error=result.position_error.item() * 100.0,
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orientation_error=result.rotation_error.item() * 100.0,
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eef_position_path_length=10,
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eef_orientation_path_length=10,
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attempts=result.attempts,
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motion_time=result.motion_time.item(),
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solve_time=result.solve_time,
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)
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if write_usd:
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# CuRobo
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q_traj = result.get_interpolated_plan()
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UsdHelper.write_trajectory_animation_with_robot_usd(
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robot_cfg,
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world,
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start_state,
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q_traj,
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dt=result.interpolation_dt,
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save_path=join_path("log/usd/", problem_name) + ".usd",
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interpolation_steps=1,
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write_robot_usd_path="log/usd/assets/",
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robot_usd_local_reference="assets/",
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base_frame="/world_" + problem_name,
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visualize_robot_spheres=True,
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)
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if write_plot:
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problem_name = problem_name
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plot_traj(
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result.optimized_plan,
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result.optimized_dt.item(),
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# result.get_interpolated_plan(),
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# result.interpolation_dt,
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title=problem_name,
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save_path=join_path("log/plot/", problem_name + ".pdf"),
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)
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plot_traj(
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# result.optimized_plan,
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# result.optimized_dt.item(),
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result.get_interpolated_plan(),
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result.interpolation_dt,
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title=problem_name,
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save_path=join_path("log/plot/", problem_name + "_int.pdf"),
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)
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# exit()
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m_list.append(current_metrics)
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all_groups.append(current_metrics)
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elif result.valid_query:
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# print("fail")
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# print(result.status)
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current_metrics = CuroboMetrics()
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debug = {
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"used_graph": result.used_graph,
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"attempts": result.attempts,
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"ik_time": result.ik_time,
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"graph_time": result.graph_time,
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"trajopt_time": result.trajopt_time,
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"total_time": result.total_time,
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"solve_time": result.solve_time,
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"status": result.status,
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"valid_query": result.valid_query,
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}
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problem["solution_debug"] = debug
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m_list.append(current_metrics)
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all_groups.append(current_metrics)
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else:
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# print("invalid: " + problem_name)
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debug = {
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"used_graph": result.used_graph,
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"attempts": result.attempts,
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"ik_time": result.ik_time,
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"graph_time": result.graph_time,
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"trajopt_time": result.trajopt_time,
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"total_time": result.total_time,
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"solve_time": result.solve_time,
|
|
"status": result.status,
|
|
"valid_query": result.valid_query,
|
|
}
|
|
problem["solution_debug"] = debug
|
|
if save_log: # and not result.success.item():
|
|
# print("save log")
|
|
UsdHelper.write_motion_gen_log(
|
|
result,
|
|
robot_cfg,
|
|
world,
|
|
start_state,
|
|
Pose.from_list(pose),
|
|
join_path("log/usd/", problem_name) + "_debug",
|
|
write_ik=False,
|
|
write_trajopt=True,
|
|
visualize_robot_spheres=False,
|
|
grid_space=2,
|
|
)
|
|
# exit()
|
|
|
|
g_m = CuroboGroupMetrics.from_list(m_list)
|
|
print(
|
|
key,
|
|
f"{g_m.success:2.2f}",
|
|
# g_m.motion_time,
|
|
g_m.time.mean,
|
|
# g_m.time.percent_75,
|
|
g_m.time.percent_98,
|
|
g_m.position_error.percent_98,
|
|
# g_m.position_error.median,
|
|
g_m.orientation_error.percent_98,
|
|
# g_m.orientation_error.median,
|
|
) # , g_m.attempts)
|
|
print(g_m.attempts)
|
|
# print("MT: ", g_m.motion_time)
|
|
# $print(ik_fail)
|
|
# exit()
|
|
# print(g_m.position_error, g_m.orientation_error)
|
|
|
|
g_m = CuroboGroupMetrics.from_list(all_groups)
|
|
print(
|
|
"All: ",
|
|
f"{g_m.success:2.2f}",
|
|
g_m.motion_time.percent_98,
|
|
g_m.time.mean,
|
|
g_m.time.percent_75,
|
|
g_m.position_error.percent_75,
|
|
g_m.orientation_error.percent_75,
|
|
) # g_m.time, g_m.attempts)
|
|
# print("MT: ", g_m.motion_time)
|
|
|
|
# print(g_m.position_error, g_m.orientation_error)
|
|
|
|
# exit()
|
|
if write_benchmark:
|
|
if not mpinets_data:
|
|
write_yaml(problems, "robometrics_mb_curobo_solution.yaml")
|
|
else:
|
|
write_yaml(problems, "robometrics_mpinets_curobo_solution.yaml")
|
|
all_files += all_groups
|
|
g_m = CuroboGroupMetrics.from_list(all_files)
|
|
# print(g_m.success, g_m.time, g_m.attempts, g_m.position_error, g_m.orientation_error)
|
|
print("######## FULL SET ############")
|
|
print("All: ", f"{g_m.success:2.2f}")
|
|
print("MT: ", g_m.motion_time)
|
|
print("PT:", g_m.time)
|
|
print("ST: ", g_m.solve_time)
|
|
print("position accuracy: ", g_m.position_error)
|
|
print("orientation accuracy: ", g_m.orientation_error)
|
|
|
|
if args.kpi:
|
|
kpi_data = {"Success": g_m.success, "Planning Time": float(g_m.time.mean)}
|
|
write_yaml(kpi_data, join_path(args.save_path, args.file_name + ".yml"))
|
|
|
|
|
|
def get_metrics_obstacles(obs: Dict[str, List[Any]]):
|
|
obs_list = []
|
|
if "cylinder" in obs and len(obs["cylinder"].items()) > 0:
|
|
for _, vi in enumerate(obs["cylinder"].values()):
|
|
obs_list.append(
|
|
Cylinder(
|
|
np.ravel(vi["pose"][:3]), vi["radius"], vi["height"], np.ravel(vi["pose"][3:])
|
|
)
|
|
)
|
|
if "cuboid" in obs and len(obs["cuboid"].items()) > 0:
|
|
for _, vi in enumerate(obs["cuboid"].values()):
|
|
obs_list.append(
|
|
Cuboid(np.ravel(vi["pose"][:3]), np.ravel(vi["dims"]), np.ravel(vi["pose"][3:]))
|
|
)
|
|
return obs_list
|
|
|
|
|
|
def check_problems(all_problems):
|
|
n_cube = 0
|
|
for problem in all_problems:
|
|
cache = WorldConfig.from_dict(problem["obstacles"]).get_obb_world().get_cache_dict()
|
|
n_cube = max(n_cube, cache["obb"])
|
|
return n_cube
|
|
|
|
|
|
if __name__ == "__main__":
|
|
parser = argparse.ArgumentParser()
|
|
parser.add_argument(
|
|
"--save_path",
|
|
type=str,
|
|
default=".",
|
|
help="path to save file",
|
|
)
|
|
parser.add_argument(
|
|
"--file_name",
|
|
type=str,
|
|
default="mg",
|
|
help="File name prefix to use to save benchmark results",
|
|
)
|
|
parser.add_argument(
|
|
"--graph",
|
|
action="store_true",
|
|
help="When True, runs only geometric planner",
|
|
default=False,
|
|
)
|
|
parser.add_argument(
|
|
"--kpi",
|
|
action="store_true",
|
|
help="When True, saves minimal metrics",
|
|
default=False,
|
|
)
|
|
|
|
parser.add_argument(
|
|
"--demo",
|
|
action="store_true",
|
|
help="When True, runs only on small dataaset",
|
|
default=False,
|
|
)
|
|
|
|
args = parser.parse_args()
|
|
setup_curobo_logger("error")
|
|
benchmark_mb(args=args, save_kpi=args.kpi)
|