Fast online learning of control regime transitions for adaptive robotic mobility

Brian Yamauchi

doi:10.1117/12.919444

9 May 2012 Fast online learning of control regime transitions for adaptive robotic mobility

Brian Yamauchi

Proceedings Volume 8387, Unmanned Systems Technology XIV; 838709 (2012) https://doi.org/10.1117/12.919444
Event: SPIE Defense, Security, and Sensing, 2012, Baltimore, Maryland, United States

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.

Citation Download Citation

Brian Yamauchi "Fast online learning of control regime transitions for adaptive robotic mobility", Proc. SPIE 8387, Unmanned Systems Technology XIV, 838709 (9 May 2012); https://doi.org/10.1117/12.919444

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