Policy gradient optimization of controllers for natural dynamic mono-pedal gait.

Abstract:

:We have previously suggested a biologically-inspired natural dynamic controller for biped locomotion, which applies torque pulses to the different joints at particular phases of an internal phase variable. The parameters of the controller, including the timing and magnitude of the torque pulses and the dynamics of the phase variable, can be kept constant in open loop or adapted to the environment in closed loop. Here we demonstrate the implementation of this approach to a mono-ped robot and the optimization of the controller parameters to enhance robustness via policy gradient. Policy gradient was applied in simulations rather than the actual robot due to safety and hardware considerations. A grounded action transformation (GAT) was learned and used to facilitate the transfer of the learned policy from simulation to hardware. We demonstrate how GAT improves the match between simulations and experiments and how learning enhances the performance and robustness of the mono-ped robot.

journal_name

Bioinspir Biomim

authors

Schallheim I,Zacksenhouse M

doi

10.1088/1748-3190/ab782a

subject

Has Abstract

pub_date

2020-03-25 00:00:00

pages

036010

issue

3

eissn

1748-3182

issn

1748-3190

journal_volume

15

pub_type

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