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The exponentially increasing advances in robotics and machine learning arefacilitating the transition of robots from being confined to controlledindustrial spaces to performing novel everyday tasks in domestic and urbanenvironments. In order to make the presence of robots safe as well ascomfortable for humans, and to facilitate their acceptance in publicenvironments, they are often equipped with social abilities for navigation andinteraction. Socially compliant robot navigation is increasingly being learnedfrom human observations or demonstrations. We argue that these techniques thattypically aim to mimic human behavior do not guarantee fair behavior. As aconsequence, social navigation models can replicate, promote, and amplifysocietal unfairness such as discrimination and segregation. In this work, weinvestigate a framework for diminishing bias in social robot navigation modelsso that robots are equipped with the capability to plan as well as adapt theirpaths based on both physical and social demands. Our proposed frameworkconsists of two components: \textit{learning} which incorporates social contextinto the learning process to account for safety and comfort, and\textit{relearning} to detect and correct potentially harmful outcomes beforethe onset. We provide both technological and societal analysis using threediverse case studies in different social scenarios of interaction. Moreover, wepresent ethical implications of deploying robots in social environments andpropose potential solutions. Through this study, we highlight the importanceand advocate for fairness in human-robot interactions in order to promote moreequitable social relationships, roles, and dynamics and consequently positivelyinfluence our society.

From Learning to Relearning: A Framework for Diminishing Bias in Social Robot Navigation