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Fairness and Bias in Robot Learning
In this work, we present the first survey on fairness in robot learning from an interdisciplinary perspective spanning technical, ethical, and legal challenges.
Doing Right by Not Doing Wrong in Human-Robot Collaboration
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.
From Learning to Relearning: A Framework for Diminishing Bias in Social Robot Navigation
The exponentially increasing advances in robotics and machine learning are facilitating the transition of robots from being confined to controlled industrial spaces to performing novel everyday tasks in domestic and urban environments.
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