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Deception Game: Closing the Safety-Learning Loop in Interactive Robot Autonomy. (arXiv:2309.01267v2 [cs.RO] UPDATED)
cs.LG updates on arXiv.org arxiv.org
An outstanding challenge for the widespread deployment of robotic systems
like autonomous vehicles is ensuring safe interaction with humans without
sacrificing performance. Existing safety methods often neglect the robot's
ability to learn and adapt at runtime, leading to overly conservative behavior.
This paper proposes a new closed-loop paradigm for synthesizing safe control
policies that explicitly account for the robot's evolving uncertainty and its
ability to quickly respond to future scenarios as they arise, by jointly
considering the physical dynamics and …
adapt arxiv autonomous autonomous vehicles autonomy behavior challenge deception deployment game humans interactive learn loop paper performance robot robotic safety systems vehicles