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.
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...
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.
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.
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%.
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.
arXiv:2607. 00218v1 Announce Type: cross Abstract: Vision-language models (VLMs) are now proposed as runtime safety guards for embodied agents in homes and factories.
arXiv:2607. 13056v1 Announce Type: cross Abstract: Current vision-language-action (VLA) benchmarks primarily evaluate isolated manipulation skills while leaving human-robot interaction structure largely unmodeled.
arXiv:2608. 05715v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) are increasingly deployed as planners in robotic systems, where they translate natural-language commands into executable actions grounded in visual scene understanding.
IndustrialVLA-Bench evaluates six released vision‑language‑action (VLA) and world‑action (WAM) robot policy models using a unified reporting schema. It measures clean capability on LIBERO, non‑language robustness on LIBERO‑Plus, instruction sensitivity on LIBERO‑Para, and observed execution cost, aggregating results across three random seeds. The benchmark also reports inference latency, peak memory, runtime mode, and evidence status for each system, enabling traceable comparisons without claiming one paradigm is universally superior.
arXiv:2610.35604v1 Announce Type: cross Abstract: Generalist robot policies and language-model agents provide complementary capabilities, but completing a task also requires adapting their use to the...
arXiv:2604.16677v2 Announce Type: replace-cross Abstract: Vision-language-action (VLA) models have emerged as generalist robotic controllers that map visual observations and language instructions to...