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- W2019934546 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." @default.
- W2019934546 created "2016-06-24" @default.
- W2019934546 creator A5036954229 @default.
- W2019934546 date "2012-05-01" @default.
- W2019934546 modified "2023-09-23" @default.
- W2019934546 title "Fast online learning of control regime transitions for adaptive robotic mobility" @default.
- W2019934546 doi "https://doi.org/10.1117/12.919444" @default.
- W2019934546 hasPublicationYear "2012" @default.
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