IEEE Robotics & Automation Magazine - June 2020 - 87

Decoding Motor Skills
of Artificial Intelligence
and Human Policies
A Study on Humanoid and
Human Balance Control

©ISTOCKPHOTO.COM/BOBBOZ

By Kai Yuan, Christopher McGreavy, Chuanyu Yang,
Wouter Wolfslag, and Zhibin Li

F

rom the advancement in computers, computeraided design has emerged for mechanical and
electronic engineering, architecture, and many
other engineering fields. Foreseeing a similar
development curve and technology wave, we
forecast a new emerging discipline in the near future that
uses learning-aided approaches to catalyze control
development, alongside other similar applications such as
medicine discovery. In this article, we propose a new
paradigm for using machine learning to facilitate quicker,
more efficient, and more effective control development, as
an alternative way of leveraging the power of machine
learning in addition to other options that intend to use
learning directly in real-world applications.
Machine learning and deep reinforcement learning (DRL),
in particular, have reached an advanced stage and can now
produce powerful policies with better autonomous performance than many state-of-the-art control and planning
approaches in robot locomotion [1], robotic manipulation
Digital Object Identifier 10.1109/MRA.2020.2980547
Date of current version: 10 April 2020

1070-9932/20©2020IEEE

[2], and even the control of complex morphological machines
[3]. DRL's ability to solve complex problems in a relatively
short development time is especially attractive. This is
empowered by training the policies that maximize the cumulative reward through the exploration of the action and state
space rather than using prior knowledge of the models about
the robot, the world, and their interactions.
To leverage the capabilities of DRL, we first develop a
DRL-based control framework to learn the rich motor skills
of push recovery for humanoid robots. The complexity in
whole-body balancing arises in challenges such as multicontact coordination based on multisensory inputs, state transitions between fully and underactuated situations, switching
policies, and generalizing to external disturbances on any
body parts and accounts for all the edge cases that a designer
has difficulty considering beforehand. In such a setting, manually designing individual control strategies and finding a reliable switching mechanism require substantial development
time, mathematical rigor, and code implementation. On the
other hand, through a well-designed DRL framework and
task-specific training procedures, a robust policy can be
learned automatically by interacting with the environment,
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IEEE ROBOTICS & AUTOMATION MAGAZINE

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IEEE Robotics & Automation Magazine - June 2020

Table of Contents for the Digital Edition of IEEE Robotics & Automation Magazine - June 2020

Contents
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