151 lines
4.6 KiB
Python
151 lines
4.6 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.sdf.world import (
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CollisionCheckerType,
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WorldCollisionConfig,
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WorldPrimitiveCollision,
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)
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from curobo.geom.sdf.world_mesh import WorldMeshCollision
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from curobo.geom.types import 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.util_file import get_robot_configs_path, get_world_configs_path, join_path, load_yaml
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from curobo.wrap.reacher.ik_solver import IKSolver, IKSolverConfig
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def ik_base_config():
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tensor_args = TensorDeviceType()
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world_file = "collision_cubby.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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ik_config = IKSolverConfig.load_from_robot_config(
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robot_cfg,
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world_cfg,
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rotation_threshold=0.05,
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position_threshold=0.005,
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num_seeds=100,
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self_collision_check=True,
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self_collision_opt=True,
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tensor_args=tensor_args,
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use_cuda_graph=False,
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n_collision_envs=1,
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collision_cache={"obb": 10},
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)
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return ik_config
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def ik_gd_config():
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tensor_args = TensorDeviceType()
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world_file = "collision_cubby.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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ik_config = IKSolverConfig.load_from_robot_config(
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robot_cfg,
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world_cfg,
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rotation_threshold=0.05,
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position_threshold=0.005,
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num_seeds=100,
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self_collision_check=True,
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self_collision_opt=True,
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tensor_args=tensor_args,
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use_cuda_graph=False,
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use_gradient_descent=True,
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grad_iters=100,
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)
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return ik_config
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def ik_es_config():
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tensor_args = TensorDeviceType()
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world_file = "collision_cubby.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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ik_config = IKSolverConfig.load_from_robot_config(
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robot_cfg,
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world_cfg,
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rotation_threshold=0.05,
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position_threshold=0.005,
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num_seeds=100,
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self_collision_check=True,
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self_collision_opt=True,
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tensor_args=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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use_fixed_samples=True,
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)
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return ik_config
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def ik_no_particle_opt_config():
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tensor_args = TensorDeviceType()
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world_file = "collision_cubby.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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ik_config = IKSolverConfig.load_from_robot_config(
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robot_cfg,
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world_cfg,
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rotation_threshold=0.05,
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position_threshold=0.005,
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num_seeds=100,
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self_collision_check=True,
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self_collision_opt=True,
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tensor_args=tensor_args,
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use_cuda_graph=False,
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use_particle_opt=False,
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grad_iters=100,
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)
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return ik_config
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@pytest.mark.parametrize(
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"config, expected",
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[
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(ik_base_config(), True),
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(ik_es_config(), True),
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(ik_gd_config(), -100), # unstable
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(ik_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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ik_solver = IKSolver(config)
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q_sample = ik_solver.sample_configs(1)
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kin_state = ik_solver.fk(q_sample)
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goal = Pose(kin_state.ee_position, kin_state.ee_quaternion)
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result = ik_solver.solve_single(goal)
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result = ik_solver.solve_single(goal)
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success = result.success
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if expected is not -100:
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assert success.item() == expected
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