悪しき振る舞いを避けることで善き協働を実現する
Doing Right by Not Doing Wrong in Human-Robot Collaboration
人とロボットの協働において、危険や不公平を生む否定的行動を避けることを学習させる新手法を提案し、社会的マニピュレーションと公平な意思決定の重要性を示した。
著者: Laura Londoño, Adrian Röfer, Tim Welschehold, Abhinav Valada
分類: cs.RO, cs.AI, cs.LG
原文アブストラクト
As robotic systems become more and more capable of assisting humans in their everyday lives, we must consider the opportunities for these artificial agents to make their human collaborators feel unsafe or to treat them unfairly. Robots can exhibit antisocial behavior causing physical harm to people or reproduce unfair behavior replicating and even amplifying historical and societal biases which are detrimental to humans they interact with. In this paper, we discuss these issues considering sociable robotic manipulation and fair robotic decision making. We propose a novel approach to learning fair and sociable behavior, not by reproducing positive behavior, but rather by avoiding negative behavior. In this study, we highlight the importance of incorporating sociability in robot manipulation, as well as the need to consider fairness in human-robot interactions.
