Self-Supervised Online Learning for Safety-Critical Control using Stereo Vision
Abstract
With the increasing prevalence of complex vision-based sensing methods for use in obstacle identification and state estimation, characterizing environment-dependent measurement errors has become a difficult and essential part of modern robotics. This paper presents a self-supervised learning approach to safety-critical control. In particular, the uncertainty associated with stereo vision is estimated, and adapted online to new visual environments, wherein this estimate is leveraged in a safety-critical controller in a robust fashion. To this end, we propose an algorithm that exploits the structure of stereo-vision to learn an uncertainty estimate without the need for ground-truth data. We then robustify existing Control Barrier Function-based controllers to provide safety in the presence of this uncertainty estimate. We demonstrate the efficacy of our method on a quadrupedal robot in a variety of environments. When not using our method safety is violated. With offline training alone we observe the robot is safe, but overly-conservative. With our online method the quadruped remains safe and conservatism is reduced.
- Publication:
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arXiv e-prints
- Pub Date:
- March 2022
- arXiv:
- arXiv:2203.01404
- Bibcode:
- 2022arXiv220301404C
- Keywords:
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- Computer Science - Robotics
- E-Print:
- 7 pages, 4 figures, conference publication at ICRA 2022