arXiv AI
LIBERO-MAX: Do Robot Policies Adapt When the World Changes?
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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.
arXiv Computer Vision
Sep 22
LIBERO-VPro: Benchmarking Closed-Loop Visual Robustness of Robotic Foundation Models
LIBERO-VPro is a benchmark designed to assess the closed‑loop visual robustness of robotic foundation models by systematically perturbing visual inputs during task execution. It spans four dimensions—Visual Evidence Degradation, Camera Staleness, Visual Source Consistency, and Task‑Relevant Scene Variation—across 12 challenge categories, 96 settings, and 3,296 task‑condition cases. Evaluations on six models over 196,000 simulated episodes and 200 real‑world rollouts show that high nominal performance can hide significant weaknesses in visual grounding, adaptation, and sensitivity to stale or missing observations.
Hugging Face Trending Papers
Sep 21
LIBERO-VPro: Benchmarking Closed-Loop Visual Robustness of Robotic Foundation Models
Robotic foundation models achieve impressive performance on standard manipulation benchmarks, yet these evaluations typically assume clean, timely, and consistent visual observations throughout execut...
arXiv AI
Jun 9
ActProbe: Action-Space Probe for Early Failure Detection of Generative Robot Policies
arXiv:2606. 08508v1 Announce Type: cross Abstract: Generative robot policies fail unpredictably at deployment: they hesitate at critical moments, drift off-task, or commit to unrecoverable actions.
arXiv AI
Jul 2
FAR: Failure-Aware Retry for Test-Time Recovery and Continual Policy Improvement
arXiv:2607. 01111v1 Announce Type: cross Abstract: Robot policies inevitably encounter failures when deployed in real environments.
arXiv AI
Aug 18
RoboLab: A High-Fidelity Simulation Benchmark for Analysis of Task Generalist Policies
arXiv:2604. 09860v4 Announce Type: replace-cross Abstract: The pursuit of general-purpose robotics has yielded impressive foundation models, yet simulation-based benchmarking remains a bottleneck due to rapid performance saturation and a lack of true generalization testing.