IEEE Robotics & Automation Magazine - June 2020 - 99

Principles From the AI-Policy" section can explain human
data across all strategies [see Figure 9(a) and (b)], as was the
case for DRL. When comparing these to the DRL results [see
Figure 8(a) and (b)] and observing the high coefficient of
determination (Table 3) it becomes apparent that the trajectories are similar for each strategy. In summary, applying policyextraction methods to humans and DRL revealed that they
are generalizable to different sources and showed that the
respective policies are very similar, as evidenced by the
MJMPC controller.

similar, which raises the question: Why use an AI policy
instead of learning from a human policy? The answer lies
in the logistics and applicability of both methods for control design. Although humans could be used as template to
gain insights on how to improve the target system
(humanoid robots), learning from an AI policy yields three
main advantages:
1) Access to all internal states: By deploying DRL on the same
system as the target system, the state space is identical. Thus,
data can be collected from DRL-learned policies as soon as
the learning process is complete and be made immediately
available for analysis. In the case of human policy analysis,
data postprocessing is highly labor intensive and requires
expensive data collection equipment to infer accurate joint
angle, joint torques, and CoM positions [17]. Data collection

Why Learn From DRL Policies Instead
of From Humans?
This section shows that the policies of DRL and humans
with their derived reversed-engineered controllers are

Robot State
100 Hz

100 Hz

500 Hz

Step Location
CoM Reference

Nonlinear Step Location Optimizer

MJMPC

Whole-Body
Control

Robot

CoM State
Figure 12. The control diagram for the robot.

(a)

(b)

(c)

Figure 13. An experimental trial. (a) The subject stands upright during the beginning of the push. (b) The subject transitions to
stepping strategy after being pushed. (c) The subject comes to a halt after the stepping action.

JUNE 2020

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IEEE ROBOTICS & AUTOMATION MAGAZINE

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99



IEEE Robotics & Automation Magazine - June 2020

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