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Title: Inferring Task-Space Central Pattern Generator Parameters for Closed-loop Control of Underactuated Robots
The complexity associated with the control of highly-articulated legged robots scales quickly as the number of joints increases. Traditional approaches to the control of these robots are often impractical for many real-time applications. This work thus presents a novel sampling-based planning ap- proach for highly-articulated robots that utilizes a probabilistic graphical model (PGM) to infer in real-time how to optimally modify goal-driven, locomotive behaviors for use in closed-loop control. Locomotive behaviors are quantified in terms of the parameters associated with a network of neural oscillators, or rather a central pattern generator (CPG). For the first time, we show that the PGM can be used to optimally modulate different behaviors in real-time (i.e., to select of optimal choice of parameter values across the CPG model) in response to changes both in the local environment and in the desired control signal. The PGM is trained offline using a library of optimal behaviors that are generated using a gradient-free optimization framework.  more » « less
Award ID(s):
1723972
NSF-PAR ID:
10195696
Author(s) / Creator(s):
; ; ;
Date Published:
Journal Name:
2020 IEEE International Conference on Robotics and Automation (ICRA)
Page Range / eLocation ID:
8833 to 8839
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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