Probabilistic Reachable-Action Verification of Visuomotor Policies via Set-Based Training

Abstract:Reachability analysis for visuomotor policies is difficult because large visual encoders make end-to-end set propagation computationally expensive and excessively conservative. We therefore freeze the visual encoder and confine set propagation to a low-dimensional interface between it and the downstream policy, with the interface set calibrated from held-out camera-pose perturbations. Propagating this set through the policy with zonotopes yields a terminal output-enclosure width that set-based training optimizes directly. During evaluation, camera-pose perturbations are sampled from the prescribed distribution, and rollout-level split conformal calibration converts the resulting action-deviation scores into a probabilistic reachable-action radius with finite-sample coverage. In controlled manipulation experiments, set-based training reduces this radius while preserving closed-loop task capability, and matched behavior-only, observational-consistency, and pointwise-adversarial controls all leave a larger radius.

Robotics (cs.RO)

arXiv:2608.02545 [cs.RO]

(or arXiv:2608.02545v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2608.02545 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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