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Balakumar Sundaralingam
2023-10-26 04:17:19 -07:00
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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 torch
a = torch.zeros(4, device="cuda:0")
# Standard Library
# Third Party
from omni.isaac.kit import SimulationApp
# CuRobo
# from curobo.wrap.reacher.ik_solver import IKSolver, IKSolverConfig
from curobo.geom.sdf.world import CollisionCheckerType
from curobo.geom.types import WorldConfig
from curobo.types.base import TensorDeviceType
from curobo.types.math import Pose
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.model.robot_world import RobotWorld, RobotWorldConfig
from curobo.wrap.reacher.motion_gen import MotionGen, MotionGenConfig, MotionGenPlanConfig
simulation_app = SimulationApp(
{
"headless": False,
"width": "1920",
"height": "1080",
}
)
# Third Party
from omni.isaac.core.utils.extensions import enable_extension
ext_list = [
"omni.kit.asset_converter",
# "omni.kit.livestream.native",
"omni.kit.tool.asset_importer",
"omni.isaac.asset_browser",
]
[enable_extension(x) for x in ext_list]
# Standard Library
import argparse
# Third Party
import carb
import numpy as np
from helper import add_robot_to_scene
from omni.isaac.core import World
from omni.isaac.core.materials import OmniPBR
from omni.isaac.core.objects import cuboid, sphere
########### OV #################
from omni.isaac.core.utils.extensions import enable_extension
from omni.isaac.core.utils.types import ArticulationAction
# CuRobo
from curobo.util.logger import setup_curobo_logger
from curobo.util.usd_helper import UsdHelper
parser = argparse.ArgumentParser()
parser.add_argument(
"--headless", action="store_true", help="When True, enables headless mode", default=False
)
parser.add_argument(
"--visualize_spheres",
action="store_true",
help="When True, visualizes robot spheres",
default=False,
)
parser.add_argument("--robot", type=str, default="franka.yml", help="robot configuration to load")
args = parser.parse_args()
def main():
usd_help = UsdHelper()
act_distance = 0.2
n_envs = 2
# assuming obstacles are in objects_path:
my_world = World(stage_units_in_meters=1.0)
my_world.scene.add_default_ground_plane()
stage = my_world.stage
usd_help.load_stage(stage)
xform = stage.DefinePrim("/World", "Xform")
stage.SetDefaultPrim(xform)
stage.DefinePrim("/curobo", "Xform")
# my_world.stage.SetDefaultPrim(my_world.stage.GetPrimAtPath("/World"))
stage = my_world.stage
# stage.SetDefaultPrim(stage.GetPrimAtPath("/World"))
# Make a target to follow
target_list = []
target_material_list = []
offset_y = 2.5
radius = 0.1
pose = Pose.from_list([0, 0, 0, 1, 0, 0, 0])
robot_cfg = load_yaml(join_path(get_robot_configs_path(), args.robot))["robot_cfg"]
robot_list = []
for i in range(n_envs):
if i > 0:
pose.position[0, 1] += offset_y
usd_help.add_subroot("/World", "/World/world_" + str(i), pose)
target = cuboid.VisualCuboid(
"/World/world_" + str(i) + "/target",
position=np.array([0.5, 0, 0.5]) + pose.position[0].cpu().numpy(),
orientation=np.array([0, 1, 0, 0]),
color=np.array([1.0, 0, 0]),
size=0.05,
)
target_list.append(target)
r = add_robot_to_scene(
robot_cfg,
my_world,
"/World/world_" + str(i) + "/",
robot_name="robot_" + str(i),
position=pose.position[0].cpu().numpy(),
)
robot_list.append(r[0])
setup_curobo_logger("warn")
# warmup curobo instance
tensor_args = TensorDeviceType()
robot_file = "franka.yml"
world_file = ["collision_test.yml", "collision_thin_walls.yml"]
world_cfg_list = []
for i in range(n_envs):
world_cfg = WorldConfig.from_dict(
load_yaml(join_path(get_world_configs_path(), world_file[i]))
) # .get_mesh_world()
world_cfg.objects[0].pose[2] -= 0.02
world_cfg.randomize_color(r=[0.2, 0.3], b=[0.0, 0.05], g=[0.2, 0.3])
usd_help.add_world_to_stage(world_cfg, base_frame="/World/world_" + str(i))
world_cfg_list.append(world_cfg)
motion_gen_config = MotionGenConfig.load_from_robot_config(
robot_cfg,
world_cfg_list,
tensor_args,
trajopt_tsteps=32,
collision_checker_type=CollisionCheckerType.MESH,
use_cuda_graph=True,
num_trajopt_seeds=12,
num_graph_seeds=12,
interpolation_dt=0.03,
collision_cache={"obb": 6, "mesh": 6},
collision_activation_distance=0.025,
acceleration_scale=1.0,
self_collision_check=True,
maximum_trajectory_dt=0.25,
fixed_iters_trajopt=True,
finetune_dt_scale=1.05,
grad_trajopt_iters=300,
minimize_jerk=False,
)
motion_gen = MotionGen(motion_gen_config)
j_names = robot_cfg["kinematics"]["cspace"]["joint_names"]
default_config = robot_cfg["kinematics"]["cspace"]["retract_config"]
print("warming up...")
motion_gen.warmup(
enable_graph=False, warmup_js_trajopt=False, batch=n_envs, batch_env_mode=True
)
config = RobotWorldConfig.load_from_config(
robot_file, world_cfg_list, collision_activation_distance=act_distance
)
model = RobotWorld(config)
i = 0
max_distance = 0.5
x_sph = torch.zeros((n_envs, 1, 1, 4), device=tensor_args.device, dtype=tensor_args.dtype)
x_sph[..., 3] = radius
env_query_idx = torch.arange(n_envs, device=tensor_args.device, dtype=torch.int32)
plan_config = MotionGenPlanConfig(
enable_graph=False, enable_graph_attempt=4, max_attempts=2, enable_finetune_trajopt=True
)
prev_goal = None
cmd_plan = [None, None]
art_controllers = [r.get_articulation_controller() for r in robot_list]
cmd_idx = 0
past_goal = None
while simulation_app.is_running():
my_world.step(render=True)
if not my_world.is_playing():
if i % 100 == 0:
print("**** Click Play to start simulation *****")
i += 1
continue
step_index = my_world.current_time_step_index
if step_index == 0:
my_world.reset()
for robot in robot_list:
idx_list = [robot.get_dof_index(x) for x in j_names]
robot.set_joint_positions(default_config, idx_list)
robot._articulation_view.set_max_efforts(
values=np.array([5000 for i in range(len(idx_list))]), joint_indices=idx_list
)
if step_index < 20:
continue
sp_buffer = []
sq_buffer = []
for k in target_list:
sph_position, sph_orientation = k.get_local_pose()
sp_buffer.append(sph_position)
sq_buffer.append(sph_orientation)
ik_goal = Pose(
position=tensor_args.to_device(sp_buffer),
quaternion=tensor_args.to_device(sq_buffer),
)
if prev_goal is None:
prev_goal = ik_goal.clone()
if past_goal is None:
past_goal = ik_goal.clone()
sim_js_names = robot_list[0].dof_names
sim_js = robot_list[0].get_joints_state()
full_js = JointState(
position=tensor_args.to_device(sim_js.positions).view(1, -1),
velocity=tensor_args.to_device(sim_js.velocities).view(1, -1) * 0.0,
acceleration=tensor_args.to_device(sim_js.velocities).view(1, -1) * 0.0,
jerk=tensor_args.to_device(sim_js.velocities).view(1, -1) * 0.0,
joint_names=sim_js_names,
)
for i in range(1, len(robot_list)):
sim_js = robot_list[i].get_joints_state()
cu_js = JointState(
position=tensor_args.to_device(sim_js.positions).view(1, -1),
velocity=tensor_args.to_device(sim_js.velocities).view(1, -1) * 0.0,
acceleration=tensor_args.to_device(sim_js.velocities).view(1, -1) * 0.0,
jerk=tensor_args.to_device(sim_js.velocities).view(1, -1) * 0.0,
joint_names=sim_js_names,
)
full_js = full_js.stack(cu_js)
prev_distance = ik_goal.distance(prev_goal)
past_distance = ik_goal.distance(past_goal)
if (
(torch.sum(prev_distance[0] > 1e-2) or torch.sum(prev_distance[1] > 1e-2))
and (torch.sum(past_distance[0]) == 0.0 and torch.sum(past_distance[1] == 0.0))
and torch.norm(full_js.velocity) < 0.01
and cmd_plan[0] is None
and cmd_plan[1] is None
):
full_js = full_js.get_ordered_joint_state(motion_gen.kinematics.joint_names)
result = motion_gen.plan_batch_env(full_js, ik_goal, plan_config.clone())
prev_goal.copy_(ik_goal)
trajs = result.get_paths()
for s in range(len(result.success)):
if result.success[s]:
cmd_plan[s] = motion_gen.get_full_js(trajs[s])
# cmd_plan = result.get_interpolated_plan()
# cmd_plan = motion_gen.get_full_js(cmd_plan)
# get only joint names that are in both:
idx_list = []
common_js_names = []
for x in sim_js_names:
if x in cmd_plan[s].joint_names:
idx_list.append(robot_list[s].get_dof_index(x))
common_js_names.append(x)
cmd_plan[s] = cmd_plan[s].get_ordered_joint_state(common_js_names)
cmd_idx = 0
# print(cmd_plan)
for s in range(len(cmd_plan)):
if cmd_plan[s] is not None and cmd_idx < len(cmd_plan[s].position):
cmd_state = cmd_plan[s][cmd_idx]
# get full dof state
art_action = ArticulationAction(
cmd_state.position.cpu().numpy(),
cmd_state.velocity.cpu().numpy(),
joint_indices=idx_list,
)
# print(cmd_state.position)
# set desired joint angles obtained from IK:
art_controllers[s].apply_action(art_action)
else:
cmd_plan[s] = None
cmd_idx += 1
past_goal.copy_(ik_goal)
for _ in range(2):
my_world.step(render=False)
if __name__ == "__main__":
main()