Auditing Robot Learning for Safety and Compliance during Deployment
- URL: http://arxiv.org/abs/2110.05702v1
- Date: Tue, 12 Oct 2021 02:40:11 GMT
- Title: Auditing Robot Learning for Safety and Compliance during Deployment
- Authors: Homanga Bharadhwaj
- Abstract summary: We study how best to audit robot learning algorithms for checking their compatibility with humans.
We believe that this is a challenging problem that will require efforts from the entire robot learning community.
- Score: 4.742825811314168
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Robots of the future are going to exhibit increasingly human-like and
super-human intelligence in a myriad of different tasks. They are also likely
going to fail and be incompliant with human preferences in increasingly subtle
ways. Towards the goal of achieving autonomous robots, the robot learning
community has made rapid strides in applying machine learning techniques to
train robots through data and interaction. This makes the study of how best to
audit these algorithms for checking their compatibility with humans, pertinent
and urgent. In this paper, we draw inspiration from the AI Safety and Alignment
communities and make the case that we need to urgently consider ways in which
we can best audit our robot learning algorithms to check for failure modes, and
ensure that when operating autonomously, they are indeed behaving in ways that
the human algorithm designers intend them to. We believe that this is a
challenging problem that will require efforts from the entire robot learning
community, and do not attempt to provide a concrete framework for auditing.
Instead, we outline high-level guidance and a possible approach towards
formulating this framework which we hope will serve as a useful starting point
for thinking about auditing in the context of robot learning.
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