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Title: Implementation of Deep Deterministic Policy Gradients for Controlling Dynamic Bipedal Walking
A control system for simulated two-dimensional bipedal walking was developed. The biped model was built based on anthropometric data. At the core of the control is a Deep Deterministic Policy Gradients (DDPG) neural network that is trained in GAZEBO, a physics simulator, to predict the ideal foot location to maintain stable walking under external impulse load. Additional controllers for hip joint movement during stance phase, and ankle joint torque during toeoff, help to stabilize the robot during walking. The simulated robot can walk at a steady pace of approximately 1m/s, and during locomotion it can maintain stability with a 30N-s impulse applied at the torso. This work implement DDPG algorithm to solve biped walking control problem. The complexity of DDPG network is decreased through carefully selected state variables and distributed control system.  more » « less
Award ID(s):
1739800
PAR ID:
10073905
Author(s) / Creator(s):
; ; ; ;
Date Published:
Journal Name:
Living Machines: Conference on Biomimetic and Biohybrid Systems
Page Range / eLocation ID:
276–287
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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