release repository

tests/robot_assets_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.cuda_robot_model.cuda_robot_model import CudaRobotModel
+from curobo.geom.types import WorldConfig
+from curobo.types.base import TensorDeviceType
+from curobo.types.math import Pose
+from curobo.types.robot import RobotConfig
+from curobo.types.state import JointState
+from curobo.util_file import get_robot_configs_path, get_world_configs_path, join_path, load_yaml
+from curobo.wrap.reacher.ik_solver import IKSolver, IKSolverConfig
+from curobo.wrap.reacher.motion_gen import MotionGen, MotionGenConfig
+
+
+@pytest.mark.parametrize(
+    "robot_file",
+    [
+        "kinova_gen3.yml",
+        "iiwa.yml",
+        "iiwa_allegro.yml",
+        "franka.yml",
+        "ur10e.yml",
+    ],
+)
+class TestRobots:
+    def test_robot_config(self, robot_file):
+        tensor_args = TensorDeviceType()
+        robot_cfg = load_yaml(join_path(get_robot_configs_path(), robot_file))["robot_cfg"]
+        robot_cfg = RobotConfig.from_dict(robot_cfg, tensor_args)
+        pass
+
+    def test_kinematics(self, robot_file):
+        tensor_args = TensorDeviceType()
+
+        robot_cfg = load_yaml(join_path(get_robot_configs_path(), robot_file))["robot_cfg"]
+        robot_cfg = RobotConfig.from_dict(robot_cfg, tensor_args)
+        robot_model = CudaRobotModel(robot_cfg.kinematics)
+        robot_cfg.cspace.inplace_reindex(robot_model.joint_names)
+        robot_model.get_state(robot_cfg.cspace.retract_config)
+        pass
+
+    def test_ik(self, robot_file):
+        world_file = "collision_table.yml"
+        tensor_args = TensorDeviceType()
+
+        robot_cfg = RobotConfig.from_dict(
+            load_yaml(join_path(get_robot_configs_path(), robot_file))["robot_cfg"]
+        )
+        world_cfg = WorldConfig.from_dict(
+            load_yaml(join_path(get_world_configs_path(), world_file))
+        )
+        ik_config = IKSolverConfig.load_from_robot_config(
+            robot_cfg,
+            world_cfg,
+            rotation_threshold=0.05,
+            position_threshold=0.005,
+            num_seeds=50,
+            self_collision_check=True,
+            self_collision_opt=True,
+            tensor_args=tensor_args,
+        )
+        ik_solver = IKSolver(ik_config)
+        b_size = 10
+        q_sample = ik_solver.sample_configs(b_size)
+        kin_state = ik_solver.fk(q_sample)
+        goal = Pose(kin_state.ee_position, kin_state.ee_quaternion)
+        result = ik_solver.solve(goal)
+        result = ik_solver.solve(goal)
+
+        success = result.success
+        assert torch.count_nonzero(success).item() >= 9.0  # we check if atleast 90% are successful
+
+    def notest_motion_gen(self, robot_file):
+        """This test causes pytest to crash when running on many robot configurations
+
+        Args:
+            robot_file: _description_
+        """
+        tensor_args = TensorDeviceType()
+
+        motion_gen_config = MotionGenConfig.load_from_robot_config(
+            robot_file,
+            "collision_table.yml",
+            tensor_args,
+            trajopt_tsteps=40,
+            use_cuda_graph=True,
+        )
+        motion_gen = MotionGen(motion_gen_config)
+        robot_cfg = load_yaml(join_path(get_robot_configs_path(), robot_file))["robot_cfg"]
+        robot_cfg = RobotConfig.from_dict(robot_cfg, tensor_args)
+
+        retract_cfg = motion_gen.get_retract_config()
+        state = motion_gen.rollout_fn.compute_kinematics(
+            JointState.from_position(retract_cfg.view(1, -1))
+        )
+
+        retract_pose = Pose(state.ee_pos_seq.squeeze(), quaternion=state.ee_quat_seq.squeeze())
+        start_state = JointState.from_position(retract_cfg.view(1, -1))
+        start_state.position[0, -1] += 0.2
+        result = motion_gen.plan(start_state, retract_pose, enable_graph=False)
+        assert result.success.item()