7 stories
VLA policies fail by grabbing the wrong object, missing grasps, closing on air, stalling and not recovering, and they get worse when cameras, layouts or wording change.
Vision-language-action (VLA) models increasingly incorporate intermediate reasoning to improve robotic manipulation, yet existing approaches primarily reason about observed states without explicitly anticipating future scene evolution.
Vision-Language-Action (VLA) models map visual observations and language instructions to continuous robot actions, but a diffusion-based action expert (AE) poses a key challenge for low-bit post-training quantization (PTQ).
The best VLA simulation benchmarks in 2026 are LIBERO-Plus or LIBERO-PRO, RoboCasa365, RoboTwin 2.0, RoboLab and SimplerEnv. Plain LIBERO is saturated at 97.1%.
Vision-language-action (VLA) policies connect a pretrained vision-language backbone to an action head through a latent interface, but which backbone layers this interface should expose remains unclear.
A practical guide to VLA policy evaluation, covering LIBERO and RoboCasa, how many rollouts you actually need, and a reproducibility checklist for citable results.
A framework for evaluating manipulation datasets, plus 12 of the best open-source datasets for training VLA models and manipulation policies.
Showing 7 of 7 stories
You're all caught up.
Get the top AI news and tools straight to your inbox every week.
Weekly · AI news
Join 15,000+ AI enthusiasts
