[Submitted on 22 Oct 2026]
Title:SEVI: Safe Evidential Real-World Reinforcement Learning
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Abstract:Real-world reinforcement learning has enabled contact-rich manipulation tasks to be trained directly on physical robots, but unsafe interactions during online learning remain a key barrier to practical deployment. We propose Safe Evidential Real-World Reinforcement Learning (SEVI), a framework for reliability-aware safety intervention during real-world policy learning. SEVI introduces an Evidential Safety Critic that estimates both future constraint-violation risk and epistemic uncertainty in a single forward pass. During online learning, SEVI evaluates each task-policy action before execution; if the action is predicted to be unsafe or the prediction is insufficiently supported by evidence, SEVI overrides it with an offline-trained recovery policy that guides the robot toward a lower-risk region. We evaluate SEVI on two simulated manipulation tasks and a real-world USB insertion task under in-distribution and distribution-shifted conditions. Across task-learning and safety-critic baselines, SEVI achieves the strongest trajectory-level safety prediction, the highest final task success, and the lowest unsafe-event counts. SEVI also reduces unsafe events by 13.4 to 57.1 percent relative to recovery-based safe RL while preserving low-latency execution-time filtering. The project page is available at: this https URL
Comments: 13 pages, 7 figures. Accepted for publication in IEEE Robotics and Automation Letters (RA-L), 2026. Project page: this https URL
Subjects:
Robotics (cs.RO); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
Cite as: arXiv:2610.31829 [cs.RO]
(or arXiv:2610.31829v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2610.31829
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Shaekh Mohammad Shithil [view email] [v1] Thu, 22 Oct 2026 19:08:36 UTC (7,468 KB)
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