arXiv Machine Learning
When Human Corrections Travel: Safety in Federated VLA Adaptation
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Learning What to Say to Your VLA: Mostly Harmless Vision Language Action Model Steering
arXiv:2606. 12299v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models provide a natural language interface to robot control, but the mapping from language to behavior is often brittle and unintuitive: semantically similar instructions can induce drastically different behaviors, while some capabilities may not be elicitable through prompting alone.
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Training-Free Action Correction for VLA Model Failures via Language Feedback
arXiv:2608.29967v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models demonstrate strong semantic understanding yet exhibit systematic failures during deployment. The conditions under...
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Towards General Language-Conditioned Latent Safety Filters
arXiv:2608. 00315v1 Announce Type: cross Abstract: Robot policies are becoming increasingly general, with vision-language-action (VLA) models enabling a single policy to execute diverse tasks specified in natural language.
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SEVI: Safe Evidential Real-World Reinforcement Learning
SEVI (Safe Evidential Real-World Reinforcement Learning) is a framework that adds a safety intervention layer to real‑world policy learning. It uses an Evidential Safety Critic to estimate both future constraint‑violation risk and epistemic uncertainty in a single forward pass, and overrides unsafe or poorly supported actions with an offline‑trained recovery policy. Experiments on simulated manipulation tasks and a real‑world USB insertion task show that SEVI improves trajectory‑level safety prediction, task success, and reduces unsafe events by 13.4–57.1% compared to other safe RL baselines.
arXiv Computer Vision
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CarryOn: A Dual-Loop Harness for Personalized and Reliable Robot Assistance
CarryOn is a dual-loop system that enhances frozen vision‑language‑action (VLA) policies for robotic assistance by adding personalization and reliable execution. The planning loop maintains persistent user and scene context to ground subtask decomposition in user preferences, while the execution loop supervises policy rollouts to prevent hazardous contact and recover from failures using reusable control skills. Experiments on a real robot and in simulation show that CarryOn reduces safety violations from 52.6% to 6.1% and increases recovery success from 13% to 80%.
arXiv AI
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Learning from Runtime Feedback through Failure-Bank Self-Evolution for Vision-Language-Action Models
The paper introduces FailBank, a four‑stage self‑evolving framework that transforms runtime feedback from safety shields into lasting policy improvements for vision‑language‑action (VLA) models. By using a counterfactual correction teacher, outcome‑aware admission, and guarded LoRA updates, FailBank converts useful shield proposals into corrective targets while preserving successful actions as anchors. Experiments on the VLA‑Arena benchmark show that FailBank boosts task success rates by up to 8.5 percentage points and reduces cumulative policy cost by up to 35.6%, outperforming both base policies and traditional runtime shielding.