77 lines
2.7 KiB
Python
77 lines
2.7 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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# CuRobo
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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 MotionGen, MotionGenConfig, MotionGenPlanConfig
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@pytest.mark.parametrize(
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"parallel_finetune, force_graph, expected_motion_time",
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[
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(True, False, 12),
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(False, False, 12),
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(True, True, 12),
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(False, True, 12),
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],
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)
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def test_pose_sequence_ur5e(parallel_finetune, force_graph, expected_motion_time):
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# load ur5e motion gen:
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world_file = "collision_table.yml"
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robot_file = "ur5e.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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interpolation_dt=(1 / 30),
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)
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motion_gen = MotionGen(motion_gen_config)
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motion_gen.warmup(parallel_finetune=parallel_finetune)
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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))
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# poses for ur5e:
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home_pose = [-0.431, 0.172, 0.348, 0, 1, 0, 0]
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pose_1 = [0.157, -0.443, 0.427, 0, 1, 0, 0]
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pose_2 = [0.126, -0.443, 0.729, 0, 0, 1, 0]
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pose_3 = [-0.449, 0.339, 0.414, -0.681, -0.000, 0.000, 0.732]
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pose_4 = [-0.449, 0.339, 0.414, 0.288, 0.651, -0.626, -0.320]
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pose_5 = [-0.218, 0.508, 0.670, 0.529, 0.169, 0.254, 0.792]
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pose_6 = [-0.865, 0.001, 0.411, 0.286, 0.648, -0.628, -0.321]
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pose_list = [home_pose, pose_1, pose_2, pose_3, pose_4, pose_5, pose_6, home_pose]
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trajectory = start_state
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motion_time = 0
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fail = 0
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for i, pose in enumerate(pose_list):
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goal_pose = Pose.from_list(pose, q_xyzw=False)
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start_state = trajectory[-1].unsqueeze(0).clone()
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start_state.velocity[:] = 0.0
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start_state.acceleration[:] = 0.0
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result = motion_gen.plan_single(
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start_state.clone(),
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goal_pose,
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plan_config=MotionGenPlanConfig(
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parallel_finetune=parallel_finetune, max_attempts=1, enable_graph=force_graph
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),
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)
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if result.success.item():
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plan = result.get_interpolated_plan()
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trajectory = trajectory.stack(plan.clone())
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motion_time += result.motion_time
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else:
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fail += 1
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assert fail == 0
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assert motion_time <= expected_motion_time
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