constrained planning, robot segmentation

tests/motion_gen_goalset_test.py

@@ -0,0 +1,201 @@
+#
+# 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.types.base import TensorDeviceType
+from curobo.types.math import Pose
+from curobo.types.robot import JointState
+from curobo.wrap.reacher.motion_gen import MotionGen, MotionGenConfig, MotionGenPlanConfig
+
+
+@pytest.fixture(scope="function")
+def motion_gen():
+    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,
+        use_cuda_graph=True,
+    )
+    motion_gen_instance = MotionGen(motion_gen_config)
+    return motion_gen_instance
+
+
+@pytest.fixture(scope="function")
+def motion_gen_batch():
+    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,
+        use_cuda_graph=True,
+    )
+    motion_gen_instance = MotionGen(motion_gen_config)
+    return motion_gen_instance
+
+
+def test_goalset_padded(motion_gen):
+    # run goalset planning
+    motion_gen.warmup(n_goalset=10)
+    motion_gen.reset()
+    m_config = MotionGenPlanConfig(False, True)
+
+    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.repeat(10, 1).view(1, -1, 3),
+        quaternion=state.ee_quat_seq.repeat(10, 1).view(1, -1, 4),
+    )
+    goal_pose.position[0, 0, 0] -= 0.1
+
+    start_state = JointState.from_position(retract_cfg.view(1, -1) + 0.3)
+
+    result = motion_gen.plan_goalset(start_state, goal_pose, m_config.clone())
+
+    assert torch.count_nonzero(result.success) == 1
+
+    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.repeat(2, 1).view(1, -1, 3),
+        quaternion=state.ee_quat_seq.repeat(2, 1).view(1, -1, 4),
+    )
+    goal_pose.position[0, 0, 0] -= 0.1
+
+    start_state = JointState.from_position(retract_cfg.view(1, -1) + 0.3)
+
+    result = motion_gen.plan_goalset(start_state, goal_pose, m_config.clone())
+
+    # run goalset with less goals:
+
+    # run single planning
+
+    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)
+
+    result = motion_gen.plan_single(start_state, goal_pose, m_config.clone())
+
+    assert torch.count_nonzero(result.success) == 1
+
+
+def test_batch_goalset_padded(motion_gen_batch):
+    motion_gen = motion_gen_batch
+    motion_gen.warmup(n_goalset=10, batch=3, enable_graph=False)
+    # run goalset planning
+    motion_gen.reset()
+
+    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.repeat(3 * 3, 1).view(3, -1, 3).contiguous(),
+        quaternion=state.ee_quat_seq.repeat(3 * 3, 1).view(3, -1, 4).contiguous(),
+    )
+    goal_pose.position[0, 1, 1] = 0.2
+    goal_pose.position[1, 0, 1] = 0.2
+    goal_pose.position[2, 1, 1] = 0.2
+
+    start_state = JointState.from_position(retract_cfg.view(1, -1) + 0.2).repeat_seeds(3)
+
+    m_config = MotionGenPlanConfig(False, True, max_attempts=10, enable_graph_attempt=20)
+    result = motion_gen.plan_batch_goalset(start_state, goal_pose, m_config.clone())
+
+    # get final solutions:
+    assert torch.count_nonzero(result.success) == result.success.shape[0]
+
+    reached_state = motion_gen.compute_kinematics(result.optimized_plan.trim_trajectory(-1))
+
+    #
+    goal_position = torch.cat(
+        [
+            goal_pose.position[x, result.goalset_index[x], :].unsqueeze(0)
+            for x in range(len(result.goalset_index))
+        ]
+    )
+
+    assert result.goalset_index is not None
+
+    assert torch.max(torch.norm(goal_position - reached_state.ee_pos_seq, dim=-1)) < 0.005
+
+    # run goalset with less goals:
+
+    motion_gen.reset()
+
+    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.repeat(6, 1).view(3, -1, 3).contiguous(),
+        quaternion=state.ee_quat_seq.repeat(6, 1).view(3, -1, 4).contiguous(),
+    )
+    goal_pose.position[0, 1, 1] = 0.2
+    goal_pose.position[1, 0, 1] = 0.2
+    goal_pose.position[2, 1, 1] = 0.2
+
+    start_state = JointState.from_position(retract_cfg.view(1, -1) + 0.2).repeat_seeds(3)
+
+    result = motion_gen.plan_batch_goalset(start_state, goal_pose, m_config)
+
+    # get final solutions:
+    assert torch.count_nonzero(result.success) == result.success.shape[0]
+
+    reached_state = motion_gen.compute_kinematics(result.optimized_plan.trim_trajectory(-1))
+
+    #
+    goal_position = torch.cat(
+        [
+            goal_pose.position[x, result.goalset_index[x], :].unsqueeze(0)
+            for x in range(len(result.goalset_index))
+        ]
+    )
+
+    assert result.goalset_index is not None
+
+    assert torch.max(torch.norm(goal_position - reached_state.ee_pos_seq, dim=-1)) < 0.005
+    # run single planning
+
+    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.squeeze(), quaternion=state.ee_quat_seq.squeeze()
+    ).repeat_seeds(3)
+
+    start_state = JointState.from_position(retract_cfg.view(1, -1) + 0.3).repeat_seeds(3)
+
+    goal_pose.position[1, 0] -= 0.1
+
+    result = motion_gen.plan_batch(start_state, goal_pose.clone(), m_config)
+    assert torch.count_nonzero(result.success) == 3
+
+    # get final solutions:
+    q = result.optimized_plan.trim_trajectory(-1).squeeze(1)
+    reached_state = motion_gen.compute_kinematics(q)
+    assert torch.norm(goal_pose.position - reached_state.ee_pos_seq) < 0.005