The paper introduces RESCUE, a counterfactual framework that evaluates whether runtime monitors for robot policies generate alarms that lead to useful interventions. By restoring simulator and policy states at checkpoints, RESCUE compares alternative actions to identify the latest checkpoint that satisfies a recovery criterion and measures whether acting on alarms reduces expected task‑completion cost. Experiments on OpenVLA‑OFT and π0.5 over LIBERO‑Spatial and LIBERO‑Plus show that recoverability depends on the state–intervention pair, and that even an ideal detector can only tolerate a 2.0–3.3% false‑alarm rate before unnecessary resets outweigh early failure stops.
arXiv:2610.32941v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models perform well on robotic manipulation, yet remain brittle once their own actions carry them into states their trai...
CoreSense is a robot‑system integration architecture that traces episodic evidence and uses a conflict‑aware belief gate to decide whether to proceed, re‑observe, abstain, or escalates. The gate evaluates scope, provenance, time, contradiction, and support before making a recommendation. Evaluation on public robot datasets, simulations, and a live cloud deployment shows that belief gating can eliminate protocol‑defined unsafe proceeds while maintaining auditability.
The paper introduces HALTER, a graph-based system that automates the reset and evaluation of long-horizon robot manipulation tasks. HALTER constructs a spatial scene graph from point clouds and vision models, uses an LLM to score rollouts, plan resets, and verify success, all without labeled success images. In experiments on a Franka arm, HALTER restores scenes in 76% of episodes, improves skill completion estimation, and reduces operator time by 72% compared to manual reset.
arXiv:2608. 03222v1 Announce Type: cross Abstract: Software engineering (SWE) agents resolve repository-level issues through long trajectories that grow increasingly expensive as context accumulates.
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.
Software engineering (SWE) agents resolve repository-level issues through long trajectories that grow increasingly expensive as context accumulates. Failed runs tend to be longer and exhibit redundant exploration or looping, suggesting that some failures may be detectable before completion.
arXiv:2607. 29169v1 Announce Type: cross Abstract: Vision-language-action (VLA) policies achieve strong performance in robotic manipulation but remain vulnerable to runtime disturbances that break the temporal alignment among visual observations, robot states, and executed actions.
arXiv:2609.36518v1 Announce Type: cross Abstract: Robots must often continue a task after a target moves, the viewpoint shifts, or an obstacle appears, even though their earlier observations and comm...
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.
arXiv:2607. 14826v1 Announce Type: cross Abstract: Safe physical AI for robot actions are required not only likely to succeed but tested to be safe before execution.
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.
The paper introduces CARE, a method for certifying accelerated inference in vision‑language‑action models. CARE uses paired rollouts on a calibration set to guarantee that the risk of acceleration‑induced failure stays below a user‑specified budget, selecting the fastest certified accelerator or falling back to the reference policy. Experiments on LIBERO suites show that CARE achieves 9.0–10.8× speedups while preserving at least 85.8% of reference‑solved episodes with 95% confidence, and it outperforms non‑guaranteed selectors in both budget adherence and rollout efficiency.