82 lines
2.6 KiB
Python
82 lines
2.6 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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# Standard Library
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# CuRobo
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# Third Party
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from torch.profiler import ProfilerActivity, profile
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# CuRobo
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from curobo.geom.sdf.world import CollisionCheckerType
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from curobo.types.base import TensorDeviceType
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from curobo.types.math import Pose
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from curobo.types.robot import JointState, RobotConfig
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from curobo.util_file import get_robot_configs_path, join_path, load_yaml
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from curobo.wrap.reacher.motion_gen import MotionGen, MotionGenConfig
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def plot_traj(trajectory):
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# Third Party
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import matplotlib.pyplot as plt
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_, axs = plt.subplots(1, 1)
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q = trajectory
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for i in range(q.shape[-1]):
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axs.plot(q[:, i], label=str(i))
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plt.legend()
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plt.show()
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def demo_motion_gen():
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PLOT = False
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tensor_args = TensorDeviceType()
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world_file = "collision_test.yml"
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robot_file = "franka.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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tensor_args,
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trajopt_tsteps=40,
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collision_checker_type=CollisionCheckerType.PRIMITIVE,
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use_cuda_graph=False,
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)
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motion_gen = MotionGen(motion_gen_config)
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robot_cfg = load_yaml(join_path(get_robot_configs_path(), robot_file))["robot_cfg"]
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robot_cfg = RobotConfig.from_dict(robot_cfg, tensor_args)
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retract_cfg = robot_cfg.cspace.retract_config
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state = motion_gen.rollout_fn.compute_kinematics(
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JointState.from_position(retract_cfg.view(1, -1))
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)
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retract_pose = Pose(state.ee_pos_seq.squeeze(), quaternion=state.ee_quat_seq.squeeze())
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start_state = JointState.from_position(retract_cfg.view(1, -1) + 0.5)
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result = motion_gen.plan(start_state, retract_pose, enable_graph=False)
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# profile:
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with profile(activities=[ProfilerActivity.CPU, ProfilerActivity.CUDA]) as prof:
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result = motion_gen.plan(start_state, retract_pose, enable_graph=False)
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print("Exporting the trace..")
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prof.export_chrome_trace("trace.json")
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exit(10)
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traj = result.raw_plan # optimized plan
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print("Trajectory Generated: ", result.success)
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if PLOT:
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plot_traj(traj.cpu().numpy())
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if __name__ == "__main__":
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demo_motion_gen()
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