# 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.
#
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# Commercial use requires sharing 50% of net profits with the copyright holder.
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# Contact: skylark0924@gmail.com
import numpy as np
from rofunc.simulator.base_sim import RobotSim
[docs]class QbSoftHandSim(RobotSim):
def __init__(self, args):
super().__init__(args)
[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(1000.0)
robot_dof_props["damping"][:].fill(180.0)
# 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 update_robot(self, traj, attractor_handles, axes_geom, sphere_geom, index, verbose=True):
from isaacgym import gymutil
for i in range(self.num_envs):
# Update attractor target from current franka state
attractor_properties = self.gym.get_attractor_properties(self.envs[i], attractor_handles[i])
pose = attractor_properties.target
# pose.p: (x, y, z), pose.r: (w, x, y, z)
pose.p.x = traj[index, 0]
pose.p.y = traj[index, 1]
pose.p.z = traj[index, 2]
pose.r.w = traj[index, 6]
pose.r.x = traj[index, 3]
pose.r.y = traj[index, 4]
pose.r.z = traj[index, 5]
self.gym.set_attractor_target(self.envs[i], attractor_handles[i], pose)
if verbose:
# Draw axes and sphere at attractor location
gymutil.draw_lines(axes_geom, self.gym, self.viewer, self.envs[i], pose)
gymutil.draw_lines(sphere_geom, self.gym, self.viewer, self.envs[i], pose)
[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)
