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Proceedings Paper

Fast online learning of control regime transitions for adaptive robotic mobility
Author(s): Brian Yamauchi
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Paper Abstract

We introduce a new framework, Model Transition Control (MTC), that models robot control problems as sets of linear control regimes linked by nonlinear transitions, and a new learning algorithm, Dynamic Threshold Learning (DTL), that learns the boundaries of these control regimes in real-time. We demonstrate that DTL can learn to prevent understeer and oversteer while controlling a simulated high-speed vehicle. We also show that DTL can enable an iRobot PackBot to avoid rollover in rough terrain and to actively shift its center-of-gravity to maintain balance when climbing obstacles. In all cases, DTL is able to learn control regime boundaries in a few minutes, often with single-digit numbers of learning trials.

Paper Details

Date Published: 9 May 2012
PDF: 12 pages
Proc. SPIE 8387, Unmanned Systems Technology XIV, 838709 (9 May 2012); doi: 10.1117/12.919444
Show Author Affiliations
Brian Yamauchi, iRobot Corp. (United States)


Published in SPIE Proceedings Vol. 8387:
Unmanned Systems Technology XIV
Robert E. Karlsen; Douglas W. Gage; Charles M. Shoemaker; Grant R. Gerhart, Editor(s)

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