arXiv AI
LIBERO-RIG: A Hardware-Setup Robustness Benchmark for Vision-Language-Action Models
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arXiv Computer Vision
Oct 9
WARP-VLA: Wrist-Camera Adaptation for View-Robust Policy Execution in Vision-Language-Action Models
WARP-VLA is a Vision‑Language‑Action model designed to be robust to variations in wrist‑camera viewpoints. It uses a Mixture‑of‑Experts architecture where each expert learns a view‑specific feature transformation, and a router selects the appropriate expert based on implicit view cues, eliminating the need for explicit camera extrinsic parameters. Experiments on the LIBERO benchmark show that WARP‑VLA doubles the success rate of pi‑0.5 from 39.2% to 78.3% under wrist‑view perturbations, and real‑robot tests confirm that the simulation‑learned adaptation transfers to diverse deployment settings.
arXiv AI
Sep 30
LIBERO-MAX: Do Robot Policies Adapt When the World Changes?
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...
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
Jul 7
From Fixed to Free Cameras: Calibration-Free View-Robust Vision-Language-Action Model
arXiv:2607. 05396v1 Announce Type: cross Abstract: Real-world robot deployment rarely maintains the training-stage camera setup, where cameras often experience repositioning or remounting depending on actual scenarios.
arXiv AI
Jul 14
See like a Robot: Robot-Centric Pointmaps for Vision-Language-Action Models
arXiv:2607. 11498v1 Announce Type: cross Abstract: Vision-language-action (VLA) models predict robot actions from visual observations and language instructions.