IEEE Robotics & Automation Magazine - June 2020 - 93

Notably, the AI is able to generalize to external disturbances it was not trained for. Figure 6 shows snapshots of Valkyrie
in which an impulse push on the shin is performed. The policy is able to generalize to this untrained case by generating a
stepping behavior. This generalization ability further extends
to the ability to recover from an impact during landing from a
0.55-m height.
The performance of the policy learned by the AI is compared with push recovery controllers in terms of maximumimpulse disturbance. The AI demonstrates the ability,
comparable to state-of-the-art push recovery controllers, to
withstand a wide range of impulse disturbances. The details of

t=0s

t = 0.06 s

the comparison are presented in [5]. In the "Benchmarking
Between Human and AI Policies" section, we further show
that the motions are human-comparable, both quantitatively
and qualitatively.
Understanding and Learning the Fundamental
Principles From the AI Policy
The idea of using AI-generated policies as inspiration for
solving hard problems has taken flight in other fields, most
notably in games such as Go, Chess, and Shogi [9] for which
the AI policy achieved superhuman-level performance. Analyzing the concepts and solution process of the AI allows

t = 0.10 s

t = 0.13 s

t = 0.16 s

F = 1,080 N

F = 1,080 N

F = 1,080 N
F = 1,080 N

(a)
t = 0.20 s

t = 0.36 s

t = 0.83 s

t = 1.06 s

t = 0.50 s

t = 0.70 s

(b)
t = 1.16 s

t = 1.46 s

(c)
Figure 6. A Valkyrie recovering from an unexpected disturbance in a test scenario never encountered during training (impulse at
the shin of 108 Ns). The learned policy naturally evolves and generates a stepping behavior. (a) A nominal starting pose (left) and
the motion during the disturbance (being pulled at the shin). (b) and (c) The recovery reactions where Valkyrie takes three steps
backwards and successfully recovers balance.

JUNE 2020

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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

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