358 lines
11 KiB
Python
358 lines
11 KiB
Python
#!/usr/bin/env python3
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#
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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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try:
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# Third Party
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import isaacsim
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except ImportError:
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pass
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# Third Party
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import torch
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a = torch.zeros(4, device="cuda:0")
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# Standard Library
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import argparse
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## import curobo:
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--headless_mode",
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type=str,
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default=None,
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help="To run headless, use one of [native, websocket], webrtc might not work.",
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)
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parser.add_argument(
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"--visualize_spheres",
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action="store_true",
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help="When True, visualizes robot spheres",
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default=False,
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)
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parser.add_argument("--robot", type=str, default="franka.yml", help="robot configuration to load")
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args = parser.parse_args()
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###########################################################
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# Third Party
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from omni.isaac.kit import SimulationApp
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simulation_app = SimulationApp(
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{
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"headless": args.headless_mode is not None,
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"width": "1920",
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"height": "1080",
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}
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)
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# Standard Library
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import os
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# Third Party
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import carb
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import numpy as np
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from helper import add_extensions, add_robot_to_scene
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from omni.isaac.core import World
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from omni.isaac.core.objects import cuboid
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########### frame prim #################
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from omni.isaac.core.utils.types import ArticulationAction
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# CuRobo
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# from curobo.wrap.reacher.ik_solver import IKSolver, IKSolverConfig
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from curobo.geom.sdf.world import CollisionCheckerType
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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
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from curobo.types.state import JointState
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from curobo.util.logger import setup_curobo_logger
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from curobo.util.usd_helper import UsdHelper
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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.mpc import MpcSolver, MpcSolverConfig
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########### OV #################
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############################################################
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########### OV #################;;;;;
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############################################################
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def draw_points(rollouts: torch.Tensor):
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if rollouts is None:
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return
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# Standard Library
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import random
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# Third Party
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try:
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from omni.isaac.debug_draw import _debug_draw
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except ImportError:
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from isaacsim.util.debug_draw import _debug_draw
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draw = _debug_draw.acquire_debug_draw_interface()
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N = 100
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# if draw.get_num_points() > 0:
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draw.clear_points()
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cpu_rollouts = rollouts.cpu().numpy()
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b, h, _ = cpu_rollouts.shape
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point_list = []
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colors = []
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for i in range(b):
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# get list of points:
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point_list += [
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(cpu_rollouts[i, j, 0], cpu_rollouts[i, j, 1], cpu_rollouts[i, j, 2]) for j in range(h)
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]
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colors += [(1.0 - (i + 1.0 / b), 0.3 * (i + 1.0 / b), 0.0, 0.1) for _ in range(h)]
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sizes = [10.0 for _ in range(b * h)]
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draw.draw_points(point_list, colors, sizes)
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def main():
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# assuming obstacles are in objects_path:
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my_world = World(stage_units_in_meters=1.0)
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stage = my_world.stage
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xform = stage.DefinePrim("/World", "Xform")
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stage.SetDefaultPrim(xform)
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stage.DefinePrim("/curobo", "Xform")
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# my_world.stage.SetDefaultPrim(my_world.stage.GetPrimAtPath("/World"))
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stage = my_world.stage
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my_world.scene.add_default_ground_plane()
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# stage.SetDefaultPrim(stage.GetPrimAtPath("/World"))
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# Make a target to follow
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target = cuboid.VisualCuboid(
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"/World/target",
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position=np.array([0.5, 0, 0.5]),
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orientation=np.array([0, 1, 0, 0]),
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color=np.array([1.0, 0, 0]),
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size=0.05,
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)
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setup_curobo_logger("warn")
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past_pose = None
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# warmup curobo instance
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usd_help = UsdHelper()
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tensor_args = TensorDeviceType()
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robot_cfg = load_yaml(join_path(get_robot_configs_path(), args.robot))["robot_cfg"]
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j_names = robot_cfg["kinematics"]["cspace"]["joint_names"]
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default_config = robot_cfg["kinematics"]["cspace"]["retract_config"]
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robot_cfg["kinematics"]["collision_sphere_buffer"] += 0.02
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robot, _ = add_robot_to_scene(robot_cfg, my_world)
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articulation_controller = robot.get_articulation_controller()
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world_cfg_table = WorldConfig.from_dict(
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load_yaml(join_path(get_world_configs_path(), "collision_table.yml"))
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)
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world_cfg_table.cuboid[0].pose[2] -= 0.04
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init_curobo = False
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tensor_args = TensorDeviceType()
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robot_cfg = load_yaml(join_path(get_robot_configs_path(), args.robot))["robot_cfg"]
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# world_cfg = WorldConfig(cuboid=world_cfg_table.cuboid, mesh=world_cfg1.mesh)
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j_names = robot_cfg["kinematics"]["cspace"]["joint_names"]
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world_cfg = WorldConfig.from_dict(
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load_yaml(join_path(get_world_configs_path(), "collision_nvblox.yml"))
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)
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world_cfg.add_obstacle(world_cfg_table.cuboid[0])
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default_config = robot_cfg["kinematics"]["cspace"]["retract_config"]
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mpc_config = MpcSolverConfig.load_from_robot_config(
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robot_cfg,
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world_cfg,
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use_cuda_graph=True,
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use_cuda_graph_metrics=True,
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use_cuda_graph_full_step=False,
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self_collision_check=True,
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collision_checker_type=CollisionCheckerType.BLOX,
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use_mppi=True,
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use_lbfgs=False,
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use_es=False,
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store_rollouts=True,
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step_dt=0.02,
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)
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mpc = MpcSolver(mpc_config)
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retract_cfg = mpc.rollout_fn.dynamics_model.retract_config.clone().unsqueeze(0)
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joint_names = mpc.rollout_fn.joint_names
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state = mpc.rollout_fn.compute_kinematics(
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JointState.from_position(retract_cfg, joint_names=joint_names)
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)
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current_state = JointState.from_position(retract_cfg, joint_names=joint_names)
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retract_pose = Pose(state.ee_pos_seq, quaternion=state.ee_quat_seq)
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goal = Goal(
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current_state=current_state,
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goal_state=JointState.from_position(retract_cfg, joint_names=joint_names),
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goal_pose=retract_pose,
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)
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goal_buffer = mpc.setup_solve_single(goal, 1)
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mpc.update_goal(goal_buffer)
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mpc_result = mpc.step(current_state, max_attempts=2)
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add_extensions(simulation_app, args.headless_mode)
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usd_help.load_stage(my_world.stage)
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usd_help.add_world_to_stage(world_cfg.get_mesh_world(), base_frame="/World")
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init_world = False
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cmd_state_full = None
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step = 0
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add_extensions(simulation_app, args.headless_mode)
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while simulation_app.is_running():
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if not init_world:
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for _ in range(10):
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my_world.step(render=True)
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init_world = True
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draw_points(mpc.get_visual_rollouts())
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my_world.step(render=True)
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if not my_world.is_playing():
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continue
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step_index = my_world.current_time_step_index
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if step_index <= 10:
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# my_world.reset()
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robot._articulation_view.initialize()
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idx_list = [robot.get_dof_index(x) for x in j_names]
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robot.set_joint_positions(default_config, idx_list)
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robot._articulation_view.set_max_efforts(
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values=np.array([5000 for i in range(len(idx_list))]), joint_indices=idx_list
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)
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if not init_curobo:
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init_curobo = True
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step += 1
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step_index = step
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# position and orientation of target virtual cube:
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cube_position, cube_orientation = target.get_world_pose()
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if past_pose is None:
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past_pose = cube_position + 1.0
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if np.linalg.norm(cube_position - past_pose) > 1e-3:
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# Set EE teleop goals, use cube for simple non-vr init:
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ee_translation_goal = cube_position
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ee_orientation_teleop_goal = cube_orientation
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ik_goal = Pose(
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position=tensor_args.to_device(ee_translation_goal),
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quaternion=tensor_args.to_device(ee_orientation_teleop_goal),
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)
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goal_buffer.goal_pose.copy_(ik_goal)
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mpc.update_goal(goal_buffer)
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past_pose = cube_position
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# if not changed don't call curobo:
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# get robot current state:
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sim_js = robot.get_joints_state()
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if sim_js is None:
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print("sim_js is None")
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continue
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js_names = robot.dof_names
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sim_js_names = robot.dof_names
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cu_js = JointState(
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position=tensor_args.to_device(sim_js.positions),
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velocity=tensor_args.to_device(sim_js.velocities) * 0.0,
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acceleration=tensor_args.to_device(sim_js.velocities) * 0.0,
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jerk=tensor_args.to_device(sim_js.velocities) * 0.0,
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joint_names=sim_js_names,
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)
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cu_js = cu_js.get_ordered_joint_state(mpc.rollout_fn.joint_names)
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if cmd_state_full is None:
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current_state.copy_(cu_js)
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else:
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current_state_partial = cmd_state_full.get_ordered_joint_state(
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mpc.rollout_fn.joint_names
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)
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current_state.copy_(current_state_partial)
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current_state.joint_names = current_state_partial.joint_names
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# current_state = current_state.get_ordered_joint_state(mpc.rollout_fn.joint_names)
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common_js_names = []
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current_state.copy_(cu_js)
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mpc_result = mpc.step(current_state, max_attempts=2)
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# ik_result = ik_solver.solve_single(ik_goal, cu_js.position.view(1,-1), cu_js.position.view(1,1,-1))
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succ = True # ik_result.success.item()
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cmd_state_full = mpc_result.js_action
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common_js_names = []
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idx_list = []
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for x in sim_js_names:
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if x in cmd_state_full.joint_names:
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idx_list.append(robot.get_dof_index(x))
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common_js_names.append(x)
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cmd_state = cmd_state_full.get_ordered_joint_state(common_js_names)
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cmd_state_full = cmd_state
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# print(ee_translation_goal, ee_orientation_teleop_goal)
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# Compute IK for given EE Teleop goals
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# articulation_action, succ = my_controller.compute_inverse_kinematics(
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# ee_translation_goal,
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# ee_orientation_teleop_goal,
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# )
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# create articulation action:
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# get full dof state
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art_action = ArticulationAction(
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cmd_state.position.cpu().numpy(),
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# cmd_state.velocity.cpu().numpy(),
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joint_indices=idx_list,
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)
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# positions_goal = articulation_action.joint_positions
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if step_index % 1000 == 0:
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print(mpc_result.metrics.feasible.item(), mpc_result.metrics.pose_error.item())
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if succ:
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# set desired joint angles obtained from IK:
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for _ in range(3):
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articulation_controller.apply_action(art_action)
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else:
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carb.log_warn("No action is being taken.")
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############################################################
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if __name__ == "__main__":
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main()
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simulation_app.close()
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