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Robots of the future are going to exhibit increasingly human-like andsuper-human intelligence in a myriad of different tasks. They are also likelygoing to fail and be incompliant with human preferences in increasingly subtleways. Towards the goal of achieving autonomous robots, the robot learningcommunity has made rapid strides in applying machine learning techniques totrain robots through data and interaction. This makes the study of how best toaudit these algorithms for checking their compatibility with humans, pertinentand urgent. In this paper, we draw inspiration from the AI Safety and Alignmentcommunities and make the case that we need to urgently consider ways in whichwe can best audit our robot learning algorithms to check for failure modes, andensure that when operating autonomously, they are indeed behaving in ways thatthe human algorithm designers intend them to. We believe that this is achallenging problem that will require efforts from the entire robot learningcommunity, and do not attempt to provide a concrete framework for auditing.Instead, we outline high-level guidance and a possible approach towardsformulating this framework which we hope will serve as a useful starting pointfor thinking about auditing in the context of robot learning.

Auditing Robot Learning for Safety and Compliance during Deployment