Task-Driven Out-of-Distribution Detection with Statistical Guarantees for Robot Learning

Alec Farid, Sushant Veer, Anirudha Majumdar

Research output: Contribution to journalConference articlepeer-review

5 Scopus citations

Abstract

Our goal is to perform out-of-distribution (OOD) detection, i.e., to detect when a robot is operating in environments that are drawn from a different distribution than the environments used to train the robot. We leverage Probably Approximately Correct (PAC)-Bayes theory in order to train a policy with a guaranteed bound on performance on the training distribution. Our key idea for OOD detection then relies on the following intuition: violation of the performance bound on test environments provides evidence that the robot is operating OOD. We formalize this via statistical techniques based on p-values and concentration inequalities. The resulting approach (i) provides guaranteed confidence bounds on OOD detection, and (ii) is task-driven and sensitive only to changes that impact the robot’s performance. We demonstrate our approach on a simulated example of grasping objects with unfamiliar poses or shapes. We also present both simulation and hardware experiments for a drone performing vision-based obstacle avoidance in unfamiliar environments (including wind disturbances and different obstacle densities). Our examples demonstrate that we can perform task-driven OOD detection within just a handful of trials. Comparisons with baselines also demonstrate the advantages of our approach in terms of providing statistical guarantees and being insensitive to task-irrelevant distribution shifts.

Original languageEnglish (US)
Pages (from-to)970-980
Number of pages11
JournalProceedings of Machine Learning Research
Volume164
StatePublished - 2021
Event5th Conference on Robot Learning, CoRL 2021 - London, United Kingdom
Duration: Nov 8 2021Nov 11 2021

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Software
  • Control and Systems Engineering
  • Statistics and Probability

Keywords

  • generalization
  • Out-of-distribution detection
  • PAC-Bayes

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