arXiv Machine Learning
If at First You Don't Succeed, Backtrack: Lightweight Recovery for Frozen VLAs
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arXiv Machine Learning
Aug 19
VLCP: Vision Language Control Policy Closed-Loop Code Replanning for Robot Manipulation
VLCP (Vision Language Control Policy) is a training‑free robot manipulation approach that keeps a vision‑language model (VLM) frozen and uses it to generate short Python control functions. Unlike traditional methods that retry a fixed policy, VLCP rewrites the control code every K steps based on multi‑view RGB, proprioceptive state, and state delta, allowing failures to be corrected within the same episode. In a 57‑task MuJoCo/RoboVerse benchmark, VLCP achieves 35.1% pooled success versus 3.5% for a single‑query baseline, with a 27.3% within‑episode recovery rate on failed grasps and efficient token usage.
arXiv AI
4d ago
RePO-VLA: Recovery-Driven Policy Optimization for Vision-Language-Action Models
RePO‑VLA is a recovery‑driven policy optimization framework for Vision‑Language‑Action models that separates success, recovery, and failure trajectories. It introduces Recovery‑Aware Initialization, a Progress‑Aware Semantic Value Function, and Value‑Conditioned Refinement to learn from both successful and failed rollouts, enabling the policy to prefer high‑progress actions without online failure detectors. The authors also present the Failure‑Recovery Benchmark (FRBench) and demonstrate that RePO‑VLA raises adversarial success rates from 20% to 75% on average in simulated and real‑world bimanual tasks.
arXiv Machine Learning
Oct 2
When Instructions Retrieve Trajectories: Diagnosing and Mitigating Generalization Failures in VLA Models
The paper investigates a specific failure mode in vision‑language‑action models, termed instruction‑action binding, where models respond to language and vision separately but fail to combine them to select the correct action under counterfactual changes. Through behavioral analyses of fine‑tuned policies, the authors show that failed rollouts often preserve source behavior or switch to other demonstrated tasks, indicating that language is not ignored but mis‑bound. They propose Equivariant Counterfactual Training (ECT), which supplies counterfactual demonstrations and a paired loss to enforce correct action selection, achieving significant performance gains across simulated and real‑world benchmarks.
arXiv AI
Jun 9
ReCoVLA: VLM-Guided Reward Compilation for Failure Recovery in Vision-Language-Action Policies
arXiv:2606. 09630v1 Announce Type: cross Abstract: Vision-language-action (VLA) policies provide strong priors for language-conditioned manipulation, but remain brittle in off-nominal states requiring targeted recovery.
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.
arXiv AI
Aug 3
ActFovea: Runtime Safeguarding for VLA Policies via Spatiotemporal Visual-Action Consistency
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.