# Copyright (C) 2024, Junjia Liu
#
# This file is part of Rofunc.
#
# Rofunc is licensed under the GNU General Public License v3.0.
# You may use, distribute, and modify this code under the terms of the GPL-3.0.
#
# Additional Terms for Commercial Use:
# Commercial use requires sharing 50% of net profits with the copyright holder.
# Financial reports and regular payments must be provided as agreed in writing.
# Non-compliance results in revocation of commercial rights.
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# For more details, see <https://www.gnu.org/licenses/>.
# Contact: skylark0924@gmail.com
import numpy as np
import torch
from rofunc.config.utils import get_sim_config
from rofunc.simulator.base_sim import RobotSim
[docs]class HumanoidSim(RobotSim):
def __init__(self, args):
super().__init__(args)
self.num_bodies = self.get_num_bodies(self.robot_asset)
# self.set_char_color(self.robot_handles, self.num_bodies, [0.54, 0.85, 0.2])
self.humanoid_asset_infos = get_sim_config(sim_name="Humanoid_info")["Model_type"]
self.humanoid_info = self._get_humanoid_info(self.robot_asset_file)
self.parts = ["hands", "upper_body", "lower_body"]
self.num_parts = len(self.parts)
self.whole_rb_dict = self.humanoid_info["rigid_bodies"]
self.wb_decompose_param_rb_ids = [
[self.whole_rb_dict[rb_name] for rb_name in self.humanoid_info["parts"][part]["rigid_bodies"]]
for part in self.parts]
self.set_colors_for_parts(self.robot_handles, self.wb_decompose_param_rb_ids)
def _get_humanoid_info(self, asset_file):
return self.humanoid_asset_infos[asset_file.split("/")[-1].split(".")[0]]
[docs] def setup_robot_dof_prop(self):
from isaacgym import gymapi
gym = self.gym
envs = self.envs
robot_asset = self.robot_asset
robot_handles = self.robot_handles
# configure robot dofs
robot_dof_props = gym.get_asset_dof_properties(robot_asset)
robot_lower_limits = robot_dof_props["lower"]
robot_upper_limits = robot_dof_props["upper"]
robot_ranges = robot_upper_limits - robot_lower_limits
robot_mids = 0.5 * (robot_upper_limits + robot_lower_limits)
robot_dof_props["driveMode"][:].fill(gymapi.DOF_MODE_POS)
robot_dof_props["stiffness"][:].fill(300.0)
robot_dof_props["damping"][:].fill(30.0)
# default dof states and position targets
robot_num_dofs = gym.get_asset_dof_count(robot_asset)
default_dof_pos = np.zeros(robot_num_dofs, dtype=np.float32)
# default_dof_pos[:] = robot_mids[:]
default_dof_state = np.zeros(robot_num_dofs, gymapi.DofState.dtype)
default_dof_state["pos"] = default_dof_pos
# # send to torch
# default_dof_pos_tensor = to_torch(default_dof_pos, device=device)
for env, robot in zip(envs, robot_handles):
# set dof properties
gym.set_actor_dof_properties(env, robot, robot_dof_props)
# set initial dof states
gym.set_actor_dof_states(env, robot, default_dof_state, gymapi.STATE_ALL)
# set initial position targets
gym.set_actor_dof_position_targets(env, robot, default_dof_pos)
[docs] def add_head_embedded_camera(self, camera_props=None, attached_body=None, local_transform=None):
from isaacgym import gymapi
if camera_props is None:
# Camera Sensor
camera_props = gymapi.CameraProperties()
camera_props.width = 1280
camera_props.height = 1280
if attached_body is None:
attached_body = "head_link2"
if local_transform is None:
local_transform = gymapi.Transform()
local_transform.p = gymapi.Vec3(0.12, 0, 0.18)
if self.PlaygroundSim.up_axis == "Y":
local_transform.r = gymapi.Quat.from_axis_angle(gymapi.Vec3(1, 0, 0), np.radians(90.0)) * \
gymapi.Quat.from_axis_angle(gymapi.Vec3(0, 0, 1), np.radians(-90.0))
elif self.PlaygroundSim.up_axis == "Z":
local_transform.r = gymapi.Quat.from_axis_angle(gymapi.Vec3(1, 0, 0), np.radians(0.0))
self.add_body_attached_camera(camera_props, attached_body, local_transform)
[docs] def show(self, visual_obs_flag=False):
"""
Visualize the CURI robot
:param visual_obs_flag: if True, show visual observation
:param camera_props: If visual_obs_flag is True, use this camera_props to config the camera
:param attached_body: If visual_obs_flag is True, use this to refer the body the camera attached to
:param local_transform: If visual_obs_flag is True, use this local transform to adjust the camera pose
"""
if visual_obs_flag:
# Setup a first-person camera embedded in CURI's head
self.add_head_embedded_camera()
super().show(visual_obs_flag)
[docs] def run_traj(self, traj, attracted_rigid_bodies=None, update_freq=0.001, verbose=True, **kwargs):
if attracted_rigid_bodies is None:
attracted_rigid_bodies = ["left_hand", "right_hand"]
self.run_traj_multi_rigid_bodies(traj, attracted_rigid_bodies, update_freq=update_freq, verbose=verbose,
**kwargs)
