133 lines
4.1 KiB
Python
133 lines
4.1 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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# Third Party
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import pytest
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import torch
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# CuRobo
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from curobo.geom.types import WorldConfig
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from curobo.rollout.rollout_base import Goal
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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 JointState, RobotConfig
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from curobo.util_file import get_robot_configs_path, get_world_configs_path, join_path, load_yaml
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from curobo.wrap.reacher.trajopt import TrajOptSolver, TrajOptSolverConfig
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def trajopt_base_config():
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tensor_args = TensorDeviceType()
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world_file = "collision_table.yml"
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robot_file = "franka.yml"
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robot_cfg = RobotConfig.from_dict(
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load_yaml(join_path(get_robot_configs_path(), robot_file))["robot_cfg"]
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)
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world_cfg = WorldConfig.from_dict(load_yaml(join_path(get_world_configs_path(), world_file)))
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trajopt_config = TrajOptSolverConfig.load_from_robot_config(
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robot_cfg,
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world_cfg,
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tensor_args,
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use_cuda_graph=False,
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use_fixed_samples=True,
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n_collision_envs=1,
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collision_cache={"obb": 10},
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seed_ratio={"linear": 0.5, "start": 0.25, "goal": 0.25},
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num_seeds=10,
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)
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return trajopt_config
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def trajopt_es_config():
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tensor_args = TensorDeviceType()
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world_file = "collision_table.yml"
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robot_file = "franka.yml"
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robot_cfg = RobotConfig.from_dict(
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load_yaml(join_path(get_robot_configs_path(), robot_file))["robot_cfg"]
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)
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world_cfg = WorldConfig.from_dict(load_yaml(join_path(get_world_configs_path(), world_file)))
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trajopt_config = TrajOptSolverConfig.load_from_robot_config(
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robot_cfg,
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world_cfg,
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tensor_args,
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use_cuda_graph=False,
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use_es=True,
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es_learning_rate=0.01,
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)
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return trajopt_config
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def trajopt_gd_config():
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tensor_args = TensorDeviceType()
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world_file = "collision_table.yml"
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robot_file = "franka.yml"
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robot_cfg = RobotConfig.from_dict(
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load_yaml(join_path(get_robot_configs_path(), robot_file))["robot_cfg"]
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)
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world_cfg = WorldConfig.from_dict(load_yaml(join_path(get_world_configs_path(), world_file)))
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trajopt_config = TrajOptSolverConfig.load_from_robot_config(
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robot_cfg,
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world_cfg,
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tensor_args,
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use_cuda_graph=False,
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use_gradient_descent=True,
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grad_trajopt_iters=500,
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)
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return trajopt_config
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def trajopt_no_particle_opt_config():
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tensor_args = TensorDeviceType()
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world_file = "collision_table.yml"
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robot_file = "franka.yml"
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robot_cfg = RobotConfig.from_dict(
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load_yaml(join_path(get_robot_configs_path(), robot_file))["robot_cfg"]
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)
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world_cfg = WorldConfig.from_dict(load_yaml(join_path(get_world_configs_path(), world_file)))
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trajopt_config = TrajOptSolverConfig.load_from_robot_config(
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robot_cfg,
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world_cfg,
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tensor_args,
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use_cuda_graph=False,
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use_particle_opt=False,
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)
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return trajopt_config
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@pytest.mark.parametrize(
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"config,expected",
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[
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(trajopt_base_config(), True),
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(trajopt_es_config(), True),
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(trajopt_gd_config(), True),
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(trajopt_no_particle_opt_config(), True),
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],
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)
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def test_eval(config, expected):
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trajopt_solver = TrajOptSolver(config)
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q_start = trajopt_solver.retract_config
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q_goal = q_start.clone() + 0.1
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kin_state = trajopt_solver.fk(q_goal)
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goal_pose = Pose(kin_state.ee_position, kin_state.ee_quaternion)
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goal_state = JointState.from_position(q_goal)
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current_state = JointState.from_position(q_start)
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js_goal = Goal(goal_pose=goal_pose, goal_state=goal_state, current_state=current_state)
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result = trajopt_solver.solve_single(js_goal)
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assert result.success.item() == expected
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