Python code for the tutorial on humanoid and wheeled-legged controllers

humanoids2022tutorial.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
#
# Copyright 2022 Stéphane Caron
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

"""
- Tutorial: https://ytazz.github.io/vnoid/humanoids2022tutorial.html
- Slides: https://scaron.info/slides/humanoids-2022.pdf
"""

import meshcat_shapes
import numpy as np
import pink.utils
from pink import apply_configuration, solve_ik
from pink.tasks import BodyTask
from pinocchio.visualize import MeshcatVisualizer
from robot_descriptions.loaders.pinocchio import load_robot_description

# Loading a robot description
robot = load_robot_description("upkie_description")
robot.setVisualizer(MeshcatVisualizer())
robot.initViewer(open=True)
robot.loadViewerModel()
robot.display(robot.q0)

# Define tasks for inverse kinematics
tasks = [
    BodyTask(f"{leg}_contact", position_cost=1.0, orientation_cost=1.0)
    for leg in ("left", "right")  # adapt to the robot you picked
]

# Initialize task targets
configuration = apply_configuration(robot, robot.q0)
for task in tasks:
    task.set_target_from_configuration(configuration)

# Display target frames
for task in tasks:
    meshcat_shapes.frame(robot.viewer[f"{task.body}_target"])


def update_targets(tasks, t, dt):
    for task in tasks:
        T = task.transform_target_to_world
        u = 0.2 * np.array([2.0, 0.0, 1.0])
        T.translation += np.sin(2 * t) * u * dt
        robot.viewer[f"{task.body}_target"].set_transform(T.np)


# Closed-loop inverse kinematics
rate = pink.utils.RateLimiter(frequency=200.0)
dt = rate.period
for t in np.arange(0.0, 5.0, dt):
    update_targets(tasks, t, dt)
    velocity = solve_ik(configuration, tasks, dt, solver="proxqp")
    q = configuration.integrate(velocity, dt)
    configuration = apply_configuration(robot, q)
    robot.display(q)
    rate.sleep()