release repository

tests/trajopt_config_test.py

@@ -0,0 +1,132 @@
+#
+# 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.rollout.rollout_base import Goal
+from curobo.types.base import TensorDeviceType
+from curobo.types.math import Pose
+from curobo.types.robot import JointState, RobotConfig
+from curobo.util_file import get_robot_configs_path, get_world_configs_path, join_path, load_yaml
+from curobo.wrap.reacher.trajopt import TrajOptSolver, TrajOptSolverConfig
+
+
+def trajopt_base_config():
+    tensor_args = TensorDeviceType()
+    world_file = "collision_table.yml"
+
+    robot_file = "franka.yml"
+    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)))
+
+    trajopt_config = TrajOptSolverConfig.load_from_robot_config(
+        robot_cfg,
+        world_cfg,
+        tensor_args,
+        use_cuda_graph=False,
+        use_fixed_samples=True,
+        n_collision_envs=1,
+        collision_cache={"obb": 10},
+        seed_ratio={"linear": 0.5, "start": 0.25, "goal": 0.25},
+        num_seeds=10,
+    )
+
+    return trajopt_config
+
+
+def trajopt_es_config():
+    tensor_args = TensorDeviceType()
+    world_file = "collision_table.yml"
+
+    robot_file = "franka.yml"
+    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)))
+
+    trajopt_config = TrajOptSolverConfig.load_from_robot_config(
+        robot_cfg,
+        world_cfg,
+        tensor_args,
+        use_cuda_graph=False,
+        use_es=True,
+        es_learning_rate=0.01,
+    )
+    return trajopt_config
+
+
+def trajopt_gd_config():
+    tensor_args = TensorDeviceType()
+    world_file = "collision_table.yml"
+
+    robot_file = "franka.yml"
+    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)))
+
+    trajopt_config = TrajOptSolverConfig.load_from_robot_config(
+        robot_cfg,
+        world_cfg,
+        tensor_args,
+        use_cuda_graph=False,
+        use_gradient_descent=True,
+        grad_trajopt_iters=500,
+    )
+    return trajopt_config
+
+
+def trajopt_no_particle_opt_config():
+    tensor_args = TensorDeviceType()
+    world_file = "collision_table.yml"
+
+    robot_file = "franka.yml"
+    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)))
+
+    trajopt_config = TrajOptSolverConfig.load_from_robot_config(
+        robot_cfg,
+        world_cfg,
+        tensor_args,
+        use_cuda_graph=False,
+        use_particle_opt=False,
+    )
+    return trajopt_config
+
+
+@pytest.mark.parametrize(
+    "config,expected",
+    [
+        (trajopt_base_config(), True),
+        (trajopt_es_config(), True),
+        (trajopt_gd_config(), True),
+        (trajopt_no_particle_opt_config(), True),
+    ],
+)
+def test_eval(config, expected):
+    trajopt_solver = TrajOptSolver(config)
+    q_start = trajopt_solver.retract_config
+    q_goal = q_start.clone() + 0.1
+    kin_state = trajopt_solver.fk(q_goal)
+    goal_pose = Pose(kin_state.ee_position, kin_state.ee_quaternion)
+    goal_state = JointState.from_position(q_goal)
+    current_state = JointState.from_position(q_start)
+    js_goal = Goal(goal_pose=goal_pose, goal_state=goal_state, current_state=current_state)
+    result = trajopt_solver.solve_single(js_goal)
+
+    assert result.success.item() == expected