331 lines
11 KiB
Python
331 lines
11 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.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.trajectory import InterpolateType
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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.motion_gen import MotionGen, MotionGenConfig, MotionGenPlanConfig
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@pytest.fixture(scope="function")
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def motion_gen():
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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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motion_gen_config = MotionGenConfig.load_from_robot_config(
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robot_file,
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world_file,
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tensor_args,
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use_cuda_graph=False,
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)
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motion_gen_instance = MotionGen(motion_gen_config)
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return motion_gen_instance
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@pytest.fixture(scope="function")
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def motion_gen_batch_env():
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tensor_args = TensorDeviceType()
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world_files = ["collision_table.yml", "collision_test.yml"]
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world_cfg = [
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WorldConfig.from_dict(load_yaml(join_path(get_world_configs_path(), world_file)))
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for world_file in world_files
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]
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robot_file = "franka.yml"
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motion_gen_config = MotionGenConfig.load_from_robot_config(
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robot_file,
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world_cfg,
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tensor_args,
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use_cuda_graph=False,
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)
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motion_gen_instance = MotionGen(motion_gen_config)
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return motion_gen_instance
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@pytest.mark.parametrize(
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"motion_gen_str,interpolation",
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[
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("motion_gen", InterpolateType.LINEAR),
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("motion_gen", InterpolateType.CUBIC),
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# ("motion_gen", InterpolateType.KUNZ_STILMAN_OPTIMAL),
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("motion_gen", InterpolateType.LINEAR_CUDA),
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],
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)
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def test_motion_gen_single(motion_gen_str, interpolation, request):
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motion_gen = request.getfixturevalue(motion_gen_str)
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motion_gen.update_interpolation_type(interpolation)
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motion_gen.warmup()
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retract_cfg = motion_gen.get_retract_config()
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state = motion_gen.compute_kinematics(JointState.from_position(retract_cfg.view(1, -1)))
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goal_pose = Pose(state.ee_pos_seq, quaternion=state.ee_quat_seq)
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start_state = JointState.from_position(retract_cfg.view(1, -1) + 0.3)
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m_config = MotionGenPlanConfig(False, True)
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result = motion_gen.plan_single(start_state, goal_pose, m_config)
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# get final solutions:
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assert torch.count_nonzero(result.success) == 1
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reached_state = motion_gen.compute_kinematics(result.optimized_plan[-1])
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assert torch.norm(goal_pose.position - reached_state.ee_pos_seq) < 0.005
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def test_motion_gen_goalset(motion_gen):
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motion_gen.warmup(n_goalset=2)
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retract_cfg = motion_gen.get_retract_config()
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state = motion_gen.compute_kinematics(JointState.from_position(retract_cfg.view(1, -1)))
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goal_pose = Pose(
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state.ee_pos_seq.repeat(2, 1).view(1, -1, 3),
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quaternion=state.ee_quat_seq.repeat(2, 1).view(1, -1, 4),
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)
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goal_pose.position[0, 0, 0] -= 0.1
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start_state = JointState.from_position(retract_cfg.view(1, -1) + 0.3)
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m_config = MotionGenPlanConfig(False, True)
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result = motion_gen.plan_goalset(start_state, goal_pose, m_config)
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# get final solutions:
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assert torch.count_nonzero(result.success) == 1
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reached_state = motion_gen.compute_kinematics(result.optimized_plan[-1])
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assert (
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torch.min(
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torch.norm(goal_pose.position[:, 0, :] - reached_state.ee_pos_seq),
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torch.norm(goal_pose.position[:, 1, :] - reached_state.ee_pos_seq),
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)
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< 0.005
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)
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assert result.goalset_index is not None
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assert (
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torch.norm(goal_pose.position[:, result.goalset_index, :] - reached_state.ee_pos_seq)
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< 0.005
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)
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def test_motion_gen_batch_goalset(motion_gen):
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motion_gen.warmup(n_goalset=3, batch=3, warmup_js_trajopt=False, enable_graph=False)
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retract_cfg = motion_gen.get_retract_config()
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state = motion_gen.compute_kinematics(JointState.from_position(retract_cfg.view(1, -1)))
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goal_pose = Pose(
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state.ee_pos_seq.repeat(6, 1).view(3, -1, 3).clone(),
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quaternion=state.ee_quat_seq.repeat(6, 1).view(3, -1, 4).clone(),
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)
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goal_pose.position[0, 1, 1] = 0.2
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goal_pose.position[1, 0, 1] = 0.2
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goal_pose.position[2, 1, 1] = 0.2
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start_state = JointState.from_position(retract_cfg.view(1, -1) + 0.2).repeat_seeds(3)
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m_config = MotionGenPlanConfig(False, True, max_attempts=1, enable_graph_attempt=None)
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result = motion_gen.plan_batch_goalset(start_state, goal_pose, m_config)
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# get final solutions:
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assert torch.count_nonzero(result.success) == result.success.shape[0]
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reached_state = motion_gen.compute_kinematics(result.optimized_plan.trim_trajectory(-1))
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#
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goal_position = torch.cat(
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[
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goal_pose.position[x, result.goalset_index[x], :].unsqueeze(0)
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for x in range(len(result.goalset_index))
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]
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)
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assert result.goalset_index is not None
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assert torch.max(torch.norm(goal_position - reached_state.ee_pos_seq, dim=-1)) < 0.005
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def test_motion_gen_batch(motion_gen):
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motion_gen.warmup(batch=2)
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retract_cfg = motion_gen.get_retract_config()
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state = motion_gen.compute_kinematics(JointState.from_position(retract_cfg.view(1, -1)))
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goal_pose = Pose(
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state.ee_pos_seq.squeeze(), quaternion=state.ee_quat_seq.squeeze()
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).repeat_seeds(2)
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start_state = JointState.from_position(retract_cfg.view(1, -1) + 0.3).repeat_seeds(2)
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goal_pose.position[1, 0] -= 0.1
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m_config = MotionGenPlanConfig(False, True)
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result = motion_gen.plan_batch(start_state, goal_pose.clone(), m_config)
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assert torch.count_nonzero(result.success) == 2
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# get final solutions:
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q = result.optimized_plan.trim_trajectory(-1).squeeze(1)
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reached_state = motion_gen.compute_kinematics(q)
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assert torch.norm(goal_pose.position - reached_state.ee_pos_seq) < 0.005
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@pytest.mark.parametrize(
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"motion_gen_str,interpolation",
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[
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("motion_gen", InterpolateType.LINEAR),
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("motion_gen", InterpolateType.CUBIC),
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# ("motion_gen", InterpolateType.KUNZ_STILMAN_OPTIMAL),
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("motion_gen", InterpolateType.LINEAR_CUDA),
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],
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)
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def test_motion_gen_batch_graph(motion_gen_str: str, interpolation: InterpolateType, request):
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motion_gen = request.getfixturevalue(motion_gen_str)
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motion_gen.graph_planner.interpolation_type = interpolation
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motion_gen.reset()
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retract_cfg = motion_gen.get_retract_config()
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state = motion_gen.compute_kinematics(JointState.from_position(retract_cfg.view(1, -1)))
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goal_pose = Pose(
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state.ee_pos_seq.squeeze(), quaternion=state.ee_quat_seq.squeeze()
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).repeat_seeds(5)
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start_state = JointState.from_position(retract_cfg.view(1, -1) + 0.3).repeat_seeds(5)
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goal_pose.position[1, 0] -= 0.05
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m_config = MotionGenPlanConfig(True, False)
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result = motion_gen.plan_batch(start_state, goal_pose, m_config)
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assert torch.count_nonzero(result.success) > 0
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# get final solutions:
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q = result.interpolated_plan.trim_trajectory(-1).squeeze(1)
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reached_state = motion_gen.compute_kinematics(q)
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assert torch.norm(goal_pose.position - reached_state.ee_pos_seq) < 0.005
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def test_motion_gen_batch_env(motion_gen_batch_env):
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motion_gen_batch_env.warmup(batch=2, batch_env_mode=True, enable_graph=False)
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# motion_gen_batch_env.reset()
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retract_cfg = motion_gen_batch_env.get_retract_config()
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state = motion_gen_batch_env.compute_kinematics(
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JointState.from_position(retract_cfg.view(1, -1))
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)
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goal_pose = Pose(
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state.ee_pos_seq.squeeze(), quaternion=state.ee_quat_seq.squeeze()
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).repeat_seeds(2)
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start_state = JointState.from_position(retract_cfg.view(1, -1) + 0.3).repeat_seeds(2)
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goal_pose.position[1, 0] -= 0.1
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m_config = MotionGenPlanConfig(False, True, max_attempts=1)
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result = motion_gen_batch_env.plan_batch_env(start_state, goal_pose, m_config)
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assert torch.count_nonzero(result.success) == 2
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# get final solutions:
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reached_state = motion_gen_batch_env.compute_kinematics(
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result.optimized_plan.trim_trajectory(-1).squeeze(1)
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)
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assert torch.norm(goal_pose.position - reached_state.ee_pos_seq) < 0.005
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def test_motion_gen_batch_env_goalset(motion_gen_batch_env):
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motion_gen_batch_env.warmup(batch=2, batch_env_mode=True, n_goalset=2, enable_graph=False)
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retract_cfg = motion_gen_batch_env.get_retract_config()
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state = motion_gen_batch_env.compute_kinematics(
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JointState.from_position(retract_cfg.view(1, -1))
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)
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goal_pose = Pose(
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state.ee_pos_seq.repeat(4, 1).view(2, -1, 3),
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quaternion=state.ee_quat_seq.repeat(4, 1).view(2, -1, 4),
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)
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start_state = JointState.from_position(retract_cfg.view(1, -1) + 0.3).repeat_seeds(2)
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goal_pose.position[1, 0] -= 0.2
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m_config = MotionGenPlanConfig(False, True, enable_graph_attempt=None)
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result = motion_gen_batch_env.plan_batch_env_goalset(start_state, goal_pose, m_config)
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assert torch.count_nonzero(result.success) > 0
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# get final solutions:
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reached_state = motion_gen_batch_env.compute_kinematics(
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result.optimized_plan.trim_trajectory(-1).squeeze(1)
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)
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assert (
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torch.min(
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torch.norm(goal_pose.position[:, 0, :] - reached_state.ee_pos_seq),
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torch.norm(goal_pose.position[:, 1, :] - reached_state.ee_pos_seq),
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)
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< 0.005
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)
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goal_position = torch.cat(
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[
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goal_pose.position[x, result.goalset_index[x], :].unsqueeze(0)
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for x in range(len(result.goalset_index))
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]
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)
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assert result.goalset_index is not None
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assert torch.max(torch.norm(goal_position - reached_state.ee_pos_seq, dim=-1)) < 0.005
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@pytest.mark.parametrize(
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"motion_gen_str,enable_graph",
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[
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("motion_gen", True),
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("motion_gen", False),
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],
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)
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def test_motion_gen_single_js(motion_gen_str, enable_graph, request):
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motion_gen = request.getfixturevalue(motion_gen_str)
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motion_gen.warmup(warmup_js_trajopt=True)
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retract_cfg = motion_gen.get_retract_config()
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start_state = JointState.from_position(retract_cfg.view(1, -1) + 0.3)
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m_config = MotionGenPlanConfig(enable_graph=enable_graph, max_attempts=2)
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goal_state = start_state.clone()
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goal_state.position -= 0.3
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result = motion_gen.plan_single_js(start_state, goal_state, m_config)
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assert torch.count_nonzero(result.success) == 1
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reached_state = result.optimized_plan[-1]
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assert torch.norm(goal_state.position - reached_state.position) < 0.05
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