315 lines
9.4 KiB
Python
315 lines
9.4 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.types.base import TensorDeviceType
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from curobo.types.math import Pose
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from curobo.types.robot import JointState
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from curobo.wrap.reacher.motion_gen import (
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MotionGen,
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MotionGenConfig,
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MotionGenPlanConfig,
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PoseCostMetric,
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)
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@pytest.fixture(scope="module")
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def motion_gen(request):
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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=True,
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project_pose_to_goal_frame=request.param,
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)
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motion_gen_instance = MotionGen(motion_gen_config)
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motion_gen_instance.warmup(enable_graph=False, warmup_js_trajopt=False)
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return motion_gen_instance
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@pytest.mark.parametrize(
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"motion_gen",
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[
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(True),
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(False),
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],
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indirect=True,
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)
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def test_approach_grasp_pose(motion_gen):
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# run full pose planning
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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 = state.ee_pose.clone()
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goal_pose.position[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(max_attempts=1)
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result = motion_gen.plan_single(start_state, goal_pose, m_config.clone())
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assert torch.count_nonzero(result.success) == 1
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# run grasp pose planning:
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m_config.pose_cost_metric = PoseCostMetric.create_grasp_approach_metric()
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result = motion_gen.plan_single(start_state, goal_pose, m_config.clone())
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assert torch.count_nonzero(result.success) == 1
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@pytest.mark.parametrize(
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"motion_gen",
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[
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(True),
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(False),
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],
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indirect=True,
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)
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def test_reach_only_position(motion_gen):
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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 = state.ee_pose.clone()
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goal_pose.position[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(
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max_attempts=1,
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pose_cost_metric=PoseCostMetric(
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reach_partial_pose=True,
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reach_vec_weight=motion_gen.tensor_args.to_device([0, 0, 0, 1, 1, 1]),
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),
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)
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result = motion_gen.plan_single(start_state, goal_pose, m_config.clone())
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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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reached_pose = motion_gen.compute_kinematics(reached_state).ee_pose.clone()
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assert goal_pose.angular_distance(reached_pose) > 0.0
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assert goal_pose.linear_distance(reached_pose) <= 0.005
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@pytest.mark.parametrize(
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"motion_gen",
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[
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(True),
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(False),
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],
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indirect=True,
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)
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def test_reach_only_orientation(motion_gen):
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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 = state.ee_pose.clone()
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goal_pose.position[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(
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max_attempts=1,
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pose_cost_metric=PoseCostMetric(
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reach_partial_pose=True,
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reach_vec_weight=motion_gen.tensor_args.to_device([1, 1, 1, 0, 0, 0]),
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),
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)
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result = motion_gen.plan_single(start_state, goal_pose, m_config.clone())
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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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reached_pose = motion_gen.compute_kinematics(reached_state).ee_pose.clone()
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assert goal_pose.linear_distance(reached_pose) > 0.0
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assert goal_pose.angular_distance(reached_pose) < 0.05
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@pytest.mark.parametrize(
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"motion_gen",
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[
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(True),
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(False),
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],
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indirect=True,
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)
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def test_hold_orientation(motion_gen):
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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 = state.ee_pose.clone()
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goal_pose.position[0, 0] -= 0.1
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start_state = JointState.from_position(retract_cfg.view(1, -1) + 0.3)
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start_pose = motion_gen.compute_kinematics(start_state).ee_pose.clone()
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goal_pose.quaternion = start_pose.quaternion.clone()
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m_config = MotionGenPlanConfig(
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max_attempts=1,
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pose_cost_metric=PoseCostMetric(
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hold_partial_pose=True,
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hold_vec_weight=motion_gen.tensor_args.to_device([1, 1, 1, 0, 0, 0]),
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),
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)
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result = motion_gen.plan_single(start_state, goal_pose, m_config.clone())
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assert torch.count_nonzero(result.success) == 1
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traj_pose = motion_gen.compute_kinematics(result.optimized_plan).ee_pose.clone()
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# assert goal_pose.linear_distance(traj_pose) > 0.0
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goal_pose = goal_pose.repeat(traj_pose.shape[0])
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assert torch.max(goal_pose.angular_distance(traj_pose)) < 0.05
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@pytest.mark.parametrize(
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"motion_gen",
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[
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(True),
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(False),
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],
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indirect=True,
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)
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def test_hold_position(motion_gen):
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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 = state.ee_pose.clone()
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goal_pose.position[0, 0] -= 0.1
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start_state = JointState.from_position(retract_cfg.view(1, -1) + 0.3)
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start_pose = motion_gen.compute_kinematics(start_state).ee_pose.clone()
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goal_pose.position = start_pose.position.clone()
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m_config = MotionGenPlanConfig(
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max_attempts=1,
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pose_cost_metric=PoseCostMetric(
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hold_partial_pose=True,
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hold_vec_weight=motion_gen.tensor_args.to_device([0, 0, 0, 1, 1, 1]),
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),
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)
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result = motion_gen.plan_single(start_state, goal_pose, m_config.clone())
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assert torch.count_nonzero(result.success) == 1
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traj_pose = motion_gen.compute_kinematics(result.optimized_plan).ee_pose.clone()
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goal_pose = goal_pose.repeat(traj_pose.shape[0])
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assert torch.max(goal_pose.linear_distance(traj_pose)) < 0.005
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@pytest.mark.parametrize(
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"motion_gen",
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[
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(False),
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(True),
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],
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indirect=True,
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)
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def test_hold_partial_pose(motion_gen):
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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 = state.ee_pose.clone()
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start_state = JointState.from_position(retract_cfg.view(1, -1) + 0.3)
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start_pose = motion_gen.compute_kinematics(start_state).ee_pose.clone()
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goal_pose.position = start_pose.position.clone()
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goal_pose.quaternion = start_pose.quaternion.clone()
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if motion_gen.project_pose_to_goal_frame:
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offset_pose = Pose.from_list([0, 0.1, 0, 1, 0, 0, 0])
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goal_pose = goal_pose.multiply(offset_pose)
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else:
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goal_pose.position[0, 1] += 0.2
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m_config = MotionGenPlanConfig(
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max_attempts=1,
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pose_cost_metric=PoseCostMetric(
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hold_partial_pose=True,
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hold_vec_weight=motion_gen.tensor_args.to_device([1, 1, 1, 1, 0, 1]),
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),
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)
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result = motion_gen.plan_single(start_state, goal_pose, m_config.clone())
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assert torch.count_nonzero(result.success) == 1
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traj_pose = motion_gen.compute_kinematics(result.optimized_plan).ee_pose.clone()
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goal_pose = goal_pose.repeat(traj_pose.shape[0])
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if motion_gen.project_pose_to_goal_frame:
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traj_pose = goal_pose.compute_local_pose(traj_pose)
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traj_pose.position[:, 1] = 0.0
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assert torch.max(traj_pose.position) < 0.005
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else:
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goal_pose.position[:, 1] = 0.0
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traj_pose.position[:, 1] = 0.0
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assert torch.max(goal_pose.linear_distance(traj_pose)) < 0.005
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@pytest.mark.parametrize(
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"motion_gen",
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[
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(False),
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(True),
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],
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indirect=True,
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)
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def test_hold_partial_pose_fail(motion_gen):
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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 = state.ee_pose.clone()
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start_state = JointState.from_position(retract_cfg.view(1, -1) + 0.3)
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start_pose = motion_gen.compute_kinematics(start_state).ee_pose.clone()
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goal_pose.position = start_pose.position.clone()
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goal_pose.quaternion = start_pose.quaternion.clone()
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if motion_gen.project_pose_to_goal_frame:
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offset_pose = Pose.from_list([0, 0.1, 0.1, 1, 0, 0, 0])
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goal_pose = goal_pose.multiply(offset_pose)
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else:
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goal_pose.position[0, 1] += 0.2
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goal_pose.position[0, 0] += 0.2
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m_config = MotionGenPlanConfig(
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max_attempts=1,
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pose_cost_metric=PoseCostMetric(
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hold_partial_pose=True,
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hold_vec_weight=motion_gen.tensor_args.to_device([1, 1, 1, 1, 0, 1]),
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),
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)
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result = motion_gen.plan_single(start_state, goal_pose, m_config.clone())
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assert torch.count_nonzero(result.success) == 0
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assert result.valid_query == False
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