Task-Driven Detection of Distribution Shifts With Statistical Guarantees for Robot Learning
Our goal is to perform out-of-distribution (OOD) detection , i.e., to detect when a robot is operating in environments drawn from a different distribution than the ones used to train the robot. We leverage probably approximately correct-Bayes theory to train a policy with a guaranteed bound on perfo...
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| Veröffentlicht in: | IEEE transactions on robotics Jg. 41; S. 926 - 945 |
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| Sprache: | Englisch |
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2025
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| Abstract | Our goal is to perform out-of-distribution (OOD) detection , i.e., to detect when a robot is operating in environments drawn from a different distribution than the ones used to train the robot. We leverage probably approximately correct-Bayes theory to train a policy with a guaranteed bound on performance on the training distribution. Our idea for OOD detection 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 <inline-formula><tex-math notation="LaTeX">p</tex-math></inline-formula>-values and concentration inequalities. The approach provides guaranteed confidence bounds on OOD detection including bounds on both the false-positive and false-negative rates of the detector and is task-driven and only sensitive to changes that impact the robot's performance. We demonstrate our approach in simulation and hardware for a grasping task using objects with unfamiliar shapes or poses and a drone performing vision-based obstacle avoidance in environments with wind disturbances and varied obstacle densities. Our examples demonstrate that we can perform task-driven OOD detection within just a handful of trials. |
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| AbstractList | Our goal is to perform out-of-distribution (OOD) detection , i.e., to detect when a robot is operating in environments drawn from a different distribution than the ones used to train the robot. We leverage probably approximately correct-Bayes theory to train a policy with a guaranteed bound on performance on the training distribution. Our idea for OOD detection 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 <inline-formula><tex-math notation="LaTeX">p</tex-math></inline-formula>-values and concentration inequalities. The approach provides guaranteed confidence bounds on OOD detection including bounds on both the false-positive and false-negative rates of the detector and is task-driven and only sensitive to changes that impact the robot's performance. We demonstrate our approach in simulation and hardware for a grasping task using objects with unfamiliar shapes or poses and a drone performing vision-based obstacle avoidance in environments with wind disturbances and varied obstacle densities. Our examples demonstrate that we can perform task-driven OOD detection within just a handful of trials. |
| Author | Pachisia, Divyanshu Majumdar, Anirudha Veer, Sushant Farid, Alec |
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| Snippet | Our goal is to perform out-of-distribution (OOD) detection , i.e., to detect when a robot is operating in environments drawn from a different distribution than... |
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| SubjectTerms | Collision avoidance Costs Deep learning in robotics and automation Detectors Drones failure detection and recovery formal methods in robotics and automation Grasping Hardware Navigation PAC-Bayes Robot sensing systems Robots Training |
| Title | Task-Driven Detection of Distribution Shifts With Statistical Guarantees for Robot Learning |
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