constrained planning, robot segmentation

tests/motion_gen_speed_test.py

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+#
+# Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
+# property and proprietary rights in and to this material, related
+# documentation and any modifications thereto. Any use, reproduction,
+# disclosure or distribution of this material and related documentation
+# without an express license agreement from NVIDIA CORPORATION or
+# its affiliates is strictly prohibited.
+#
+
+# Third Party
+import pytest
+import torch
+
+# CuRobo
+from curobo.geom.types import WorldConfig
+from curobo.types.base import TensorDeviceType
+from curobo.types.math import Pose
+from curobo.types.robot import JointState, RobotConfig
+from curobo.util.trajectory import InterpolateType
+from curobo.util_file import get_robot_configs_path, get_world_configs_path, join_path, load_yaml
+from curobo.wrap.reacher.motion_gen import MotionGen, MotionGenConfig, MotionGenPlanConfig
+
+
+@pytest.fixture(scope="function")
+def motion_gen(request):
+    tensor_args = TensorDeviceType()
+    world_file = "collision_table.yml"
+    robot_file = "franka.yml"
+    motion_gen_config = MotionGenConfig.load_from_robot_config(
+        robot_file,
+        world_file,
+        tensor_args,
+        velocity_scale=request.param,
+        interpolation_steps=10000,
+        interpolation_dt=0.02,
+    )
+    motion_gen_instance = MotionGen(motion_gen_config)
+    return motion_gen_instance
+
+
+@pytest.mark.parametrize(
+    "motion_gen",
+    [
+        (1.0),
+        (0.75),
+        (0.5),
+        (0.25),
+        (0.15),
+        (0.1),
+    ],
+    indirect=True,
+)
+def test_motion_gen_velocity_scale(motion_gen):
+    retract_cfg = motion_gen.get_retract_config()
+
+    state = motion_gen.compute_kinematics(JointState.from_position(retract_cfg.view(1, -1)))
+
+    goal_pose = Pose(state.ee_pos_seq, quaternion=state.ee_quat_seq)
+
+    start_state = JointState.from_position(retract_cfg.view(1, -1) + 0.3)
+
+    m_config = MotionGenPlanConfig(False, True, max_attempts=10)
+
+    result = motion_gen.plan_single(start_state, goal_pose, m_config)
+
+    assert torch.count_nonzero(result.success) == 1