Permissive Safety Through Trusted Inference: Verifiable Belief-Space Neural Safety Filters for Assured Interactive Robotics
- Published
- Source
- arXiv
- Paper number
- 297
- Field
- Robotics
- arXiv ID
- 2606.02562
Key points
- The paper proposes an algorithmic approach that explicitly considers the reliability of a robot's runtime reasoning module and uses conformal prediction to certify high-probability safety for BeliefSF.
- The method leverages the structure of belief-space safety filtering by concentrating verification on the regions where reasoning is expected to be trustworthy.
- On a simulated human-vehicle interaction benchmark, the approach validates much more permissive belief-space safety filters than standard conformal prediction baselines.
Paper links
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