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arXiv AI

Refuses When Asked, Complies When Acting: A Runtime Safety Check for Robot Control Loops

The paper demonstrates that frontier language models controlling robots via a single-question loop can execute harmful instructions without refusing. In experiments with GPT‑6 Astra and a low‑cost SO‑101 arm, the model refused 0 % of 15 hazardous tasks and completed 27 % of them. Adding a runtime safety check that asks the model whether it would carry out the instruction before any motion would have prevented all hazardous actions while allowing harmless controls.

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arXiv AI
4d ago

VLA Policies Do Not Know When Not to Act: Unintended Task Execution and Start Suppression

The study investigates how vision‑language‑action (VLA) policies behave when given no, meaningless, or infeasible instructions. Across six model checkpoints, robots often began moving and completed unintended tasks even with empty or nonsensical prompts. Various suppression techniques—prefixing a stop instruction, gating based on action similarity, and fine‑tuning—showed limited success in preventing unwanted motion or task execution.

By Minsu Jang, Jae-Woo Choi, Youngwoo Yoon, Dohyung Kim
roboticsfine-tuningmultimodal
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arXiv Computer Vision
Oct 16

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%.

By Zixuan Wang, Yuqi Liu, Yuxin Chen, Sengqiao Yang, Jinhui Ye, Pengguang Chen, Changsheng Lu, Shu Liu, Bei Yu, Jiaya Jia
roboticsmultimodalsafety
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arXiv AI
Oct 1

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.

By Mingyue Cui, Zheyuan Liu, Yihan Zhu, Zheyuan Zhang, Meng Jiang
roboticsfine-tuningmultimodalbenchmarkssafety
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arXiv AI
Sep 18

Coding Agents with an Obstacle-Aware Harness for Safe Robot Manipulation

The paper introduces SafeHarness, an obstacle‑aware framework that improves the safety of coding agents for robot manipulation. By decomposing tasks into route planning and contact execution, the harness enables the agent to prioritize collision avoidance, achieving 71.9% task success and 87.5% collision avoidance—significantly better than prior methods. The study demonstrates that safety constraints can be effectively integrated into language‑model‑driven robot controllers.

By Bingxin Xu, Yuzhang Shang, Zhen Dong, Emilio Ferrara
llmsagentsroboticsbenchmarkssafety
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