Adapt On-the-Go: Behavior Modulation for Single-Life Robot Deployment
Annie S. Chen Govind Chada Laura Smith Archit Sharma Zipeng Fu Sergey Levine Chelsea Finn
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.