Doing Right by Not Doing Wrong in Human-Robot Collaboration
This addresses safety and fairness issues for humans interacting with robots, but appears incremental as it builds on existing concerns in sociable manipulation and fair decision-making.
The paper tackles the problem of robots causing harm or unfairness in human-robot collaboration by proposing an approach that learns fair and sociable behavior through avoiding negative actions, rather than replicating positive ones.
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.