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mimic_util.py
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# -*- coding: utf-8 -*-
# Author：Qiwei Wu
# Email: nathan.wuqw@gmail.com
# Date：2023-12
import urdfpy
import os
import torch
def find_joints_with_dof(asset_root, asset_file, selected_joint_names):
#In this function, only the selected joints will be processed
#This function categorizes these joints into two groups and saves their attributes in a dictionary
#urdf_paths
urdf_path = os.path.join(asset_root, asset_file)
#using urdfpy to load urdf files
robot = urdfpy.URDF.load(urdf_path)
joints_with_dof = []
mimic_joints = []
actuators = []
Dofs = 0
for joint_name in selected_joint_names:
joint = robot.joint_map[joint_name]
if joint.joint_type != "fixed":
Dofs += 1
joint_id = Dofs
joint_info = {
"id": joint_id,
"name": joint_name,
"type": joint.joint_type,
"origin": joint.origin,
"parent_link": joint.parent,
"child_link": joint.child,
"axis": joint.axis,
"calibration": getattr(joint, 'calibration', None),
"dynamics": getattr(joint, 'dynamics', None),
"limit": getattr(joint, 'limit', None),
"mimic": getattr(joint, 'mimic', None),
"safety_controller": getattr(joint, 'safety_controller', None)
}
joints_with_dof.append(joint_info)
if joint.mimic:
mimic_info = {
"id": joint_id,
"name": joint_name,
"actuator": getattr(joint.mimic, 'joint'),
"multiplier": getattr(joint.mimic, 'multiplier', None),
"offset": getattr(joint.mimic, 'offset', None)
}
mimic_joints.append(mimic_info)
else:
actuator_info = {
"id": joint_id,
"name": joint_name,
}
actuators.append(actuator_info)
return joints_with_dof, mimic_joints, actuators, Dofs
def actuate(actuators, mimic_joints, actuated_dof, u_delta, u_act):
#This function automatically assigns values to individual actuated joints
# and mimic joints in the order specified by selected_joint_names.
#actuators: dict
#mimic_joints: dict
#actuated_dof: int, joints
#u_delta: tensor or ndarray, matrix
# Required Format [actuated:unactuated],
# shape(num_envs, dof of selected actuators + dof of selected mimic joints)
#u_act: Control Signal for actuators,
# shape(num_envs, dof of selected actuators)
unactuated_dof = len(actuators) - actuated_dof
for i in range(unactuated_dof):
#for actuators
u_delta[:, actuators[actuated_dof+i]["id"]-1] = u_act[:, i]
#for mimic_joints
for item in mimic_joints:
if item.get("actuator") == actuators[actuated_dof+i]["name"]:
u_delta[:, item["id"]-1] = u_act[:, i] * item["multiplier"] + item["offset"]
return u_delta
def position_check(actuators, mimic_joints, actuated_dof, dof_pos):
# this function is used for close-loop control for adaptive gripper
# u_offset is the error between current_gripper_dof_pos and target_gripper_dof_pos
origin_pos = dof_pos.clone()
unactuated_dof = len(actuators) - actuated_dof
target_pos = dof_pos.clone()
for i in range(unactuated_dof):
#for actuators
target_pos[:, actuators[actuated_dof+i]["id"]-1] = origin_pos[:, actuated_dof+i]
#for mimic_joints
for item in mimic_joints:
if item.get("actuator") == actuators[actuated_dof+i]["name"]:
target_pos[:, item["id"]-1] = origin_pos[:, actuated_dof+i] * item["multiplier"] + item["offset"]
u_offset = target_pos-origin_pos
return u_offset
def mimic_clip(actuators, mimic_joints,actuated_dof, all_limits, action_limits):
#this function is used for setting limitations
unactuated_dof = len(actuators) - actuated_dof
all_limits[:,:actuated_dof] = action_limits[:,:actuated_dof]
for i in range(unactuated_dof):
#for actuators
all_limits[:,actuators[actuated_dof+i]["id"]-1] = action_limits[:,actuated_dof+i]
#for mimic_joints
for item in mimic_joints:
if item.get("actuator") == actuators[actuated_dof+i]["name"]:
all_limits[:,item["id"]-1] = action_limits[:,actuated_dof+i] * item["multiplier"]
if item["multiplier"] < 0:
all_limits[[0,1],item["id"]-1]=all_limits[[1,0],item["id"]-1]
return all_limits
if __name__ == "__main__":
asset_root = "./assets"
asset_file = "ur5_rq.urdf"
selected_joint_names = ['shoulder_pan_joint', 'shoulder_lift_joint', 'elbow_joint', 'wrist_1_joint', 'wrist_2_joint', 'wrist_3_joint', 'finger_joint', 'left_inner_finger_joint', 'left_inner_knuckle_joint', 'right_inner_knuckle_joint', 'right_outer_knuckle_joint', 'right_inner_finger_joint']
joints_with_dof, mimic_joints, actuators, dof = find_joints_with_dof(asset_root, asset_file, selected_joint_names)
u_delta = torch.tensor([[-0.3495, -0.6570, 0.5364, 0.0082, -0.0630, 0.0755, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000],
[-0.0476, -0.0871, -0.0787, -0.1867, -0.0776, 0.5742, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000],
[-0.0358, -0.1763, -0.0809, -0.0278, -0.1416, 0.4039, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000],
[-0.0363, -0.3671, 0.2545, 0.0325, -0.1595, 0.2221, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000]])
u_act = torch.tensor([[0.5],[0.5],[0.1],[0.5]])
actuated_dof = 6 #arm dof
print("Dof of selected joints:", dof)
print("\n")
if joints_with_dof:
print("Revolute Joints:")
for joint_info in joints_with_dof:
print(f"Joint ID: {joint_info['id']}")
print(f"- Joint Name: {joint_info['name']}")
print(f"- Joint Type: {joint_info['type']}")
print(f"- Origin: {joint_info['origin']}")
print(f"- Parent Link: {joint_info['parent_link']}")
print(f"- Child Link: {joint_info['child_link']}")
print(f"- Axis: {joint_info['axis']}")
print(f"- Limit: {joint_info['limit']}")
print(f"- Mimic: {joint_info['mimic']}")
print("\n")
for mimic_info in mimic_joints:
print(f"Mimic Joint ID: {mimic_info['id']}")
print(f"- Joint Name: {mimic_info['name']}")
print(f"- actuator: {mimic_info['actuator']}")
print(f"- multiplier: {mimic_info['multiplier']}")
print(f"- offset: {mimic_info['offset']}")
print("\n")
for actuator_info in actuators:
print(f"Actuator Joint ID: {actuator_info['id']}")
print(f"- Joint Name: {actuator_info['name']}")
print("\n")
else:
print("No revolute joints found.")
u_delta = actuate(actuators, mimic_joints, actuated_dof, u_delta, u_act)
print("u_delta_new: ", u_delta)