123 lines
3.9 KiB
Python
123 lines
3.9 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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import time
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# Third Party
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import torch
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# CuRobo
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from curobo.geom.types import WorldConfig
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from curobo.rollout.rollout_base import Goal
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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, get_world_configs_path, join_path, load_yaml
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from curobo.wrap.reacher.ik_solver import IKSolver, IKSolverConfig
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from curobo.wrap.reacher.trajopt import TrajOptSolver, TrajOptSolverConfig
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def plot_js(trajectory: JointState):
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# Third Party
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import matplotlib.pyplot as plt
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_, axs = plt.subplots(4, 1)
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q = trajectory.position.cpu().numpy()
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qd = trajectory.velocity.cpu().numpy()
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qdd = trajectory.acceleration.cpu().numpy()
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qddd = None
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if trajectory.jerk is not None:
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qddd = trajectory.jerk.cpu().numpy()
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for i in range(q.shape[-1]):
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axs[0].plot(q[:, i], label=str(i))
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axs[1].plot(qd[:, i], label=str(i))
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axs[2].plot(qdd[:, i], label=str(i))
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if qddd is not None:
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axs[3].plot(qddd[:, i], label=str(i))
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plt.legend()
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plt.show()
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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_trajopt_collision_free():
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PLOT = True
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tensor_args = TensorDeviceType()
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world_file = "collision_table.yml"
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robot_file = "franka.yml"
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robot_cfg = RobotConfig.from_dict(
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load_yaml(join_path(get_robot_configs_path(), robot_file))["robot_cfg"]
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)
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world_cfg = WorldConfig.from_dict(load_yaml(join_path(get_world_configs_path(), world_file)))
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trajopt_config = TrajOptSolverConfig.load_from_robot_config(
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robot_cfg,
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world_cfg,
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tensor_args,
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use_cuda_graph=False,
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)
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trajopt_solver = TrajOptSolver(trajopt_config)
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q_start = trajopt_solver.retract_config
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q_goal = q_start.clone() + 0.1
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# q_goal[...,-1] -=0.2
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kin_state = trajopt_solver.fk(q_goal)
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goal_pose = Pose(kin_state.ee_position, kin_state.ee_quaternion)
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goal_state = JointState.from_position(q_goal)
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current_state = JointState.from_position(q_start)
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# do single planning:
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# print("Running Goal State trajopt")
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# js_goal = Goal(goal_state=goal_state, current_state=current_state)
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# result = trajopt_solver.solve_single(js_goal)
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# traj = result.solution.position
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# print(result.success, result.cspace_error)
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# print(goal_state.position)
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# print(traj[...,-1,:])
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# print(torch.linalg.norm((goal_state.position - traj[...,-1,:])).item())
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# exit()
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# if PLOT:
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# #plot_traj(traj)
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# plot_js(result.solution)
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# exit()
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print("Running Goal Pose trajopt")
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js_goal = Goal(goal_pose=goal_pose, current_state=current_state)
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result = trajopt_solver.solve_single(js_goal)
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print(result.success)
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if PLOT:
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plot_js(result.solution)
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# run goalset planning:
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print("Running Goal Pose Set trajopt")
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# g_set = Pose(kin_state.ee_position, kin_state.ee_quaternion.repeat(2,1).view())
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# js_goal = Goal(goal_pose=goal_pose, current_state=current_state)
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# result = trajopt_solver.solve_single(js_goal)
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if __name__ == "__main__":
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# demo_basic_ik()
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# demo_full_config_collision_free_ik()
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# demo_full_config_batch_env_collision_free_ik()
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demo_trajopt_collision_free()
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