تقرير
Self-Supervised Online Learning for Safety-Critical Control using Stereo Vision
| العنوان: | Self-Supervised Online Learning for Safety-Critical Control using Stereo Vision |
|---|---|
| المؤلفون: | Cosner, Ryan K., Rodriguez, Ivan D. Jimenez, Molnar, Tamas G., Ubellacker, Wyatt, Yue, Yisong, Ames, Aaron D., Bouman, Katherine L. |
| سنة النشر: | 2022 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Robotics |
| الوصف: | 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. Comment: 7 pages, 4 figures, conference publication at ICRA 2022 |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2203.01404 |
| رقم الأكسشن: | edsarx.2203.01404 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |