Adapt On-the-Go: Behavior Modulation for Single-Life Robot Deployment
Primary Area: reinforcement learning
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Keywords: reinforcement learning, single-life deployment, autonomous reinforcement learning, out-of-distribution adaptation
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TL;DR: We propose Robust Autonomous Modulation (ROAM), a simple framework for efficiently leveraging pre-trained behaviors to adapt to changing situations at deployment time.
Abstract: To succeed in the real world, robots must cope with situations that differ from those seen during training. We study the problem of adapting on-the-fly to such novel scenarios during deployment, by drawing upon a diverse repertoire of previously- learned behaviors. Our approach, RObust Autonomous Modulation (ROAM), introduces a mechanism based on the perceived value of pre-trained behaviors to select and adapt pre-trained behaviors to the situation at hand. Crucially, this adaptation process all happens within a single episode at test time, without any human supervision. We provide theoretical analysis of our selection mechanism and demonstrate that ROAM enables a robot to adapt rapidly to changes in dynamics both in simulation and on a real Go1 quadruped, even successfully moving forward with roller skates on its feet. Our approach adapts over 2x as efficiently compared to existing methods when facing a variety of out-of-distribution situations during deployment by effectively choosing and adapting relevant behaviors on-the-fly.
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Submission Number: 6577
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