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
arXiv:2609.38411v1 Announce Type: new
Abstract: Leading AI developers have reported agents acting beyond their approved limits, which a United Nations panel described as an early warning of loss of h...
By Mohamed Aly Bouke
arXiv:2609.20822v2 Announce Type: replace-cross
Abstract: Coding agents have emerged as a promising paradigm for robot manipulation: a language model writes the robot controller as a program, and age...
By Bingxin Xu, Yuzhang Shang, Zhen Dong, Emilio Ferrara
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
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
arXiv:2609.21942v1 Announce Type: cross
Abstract: A robot that fails at a task faces the first decision in corrective dialogue: act on its own diagnosis, consult another onboard sensor, or interrupt...
By Eshika Pathak, Leela Krishna