Researchers introduced Retriever, a programming framework for closed-loop robot agents that supports modules running on independent clocks from milliseconds to seconds, and demonstrated Retriever-0, a…
Researchers introduced Task-Agnostic Pretraining (TAP), a two-stage framework that learns motor skills from unlabeled interaction data and grounds them in language with minimal expert demonstrations, …
Researchers propose ImageWAM, a world action model that replaces video generation with image editing for robot control, achieving superior performance while reducing computational cost to 1/6 FLOPs an…
Researchers have developed PersonaDrive, a pipeline that conditions vision-language-action driving agents on retrieved demonstrations from a style-instructed human driving dataset to produce aggressiv…
Researchers have identified a deployment-safety gap in vision-language-action (VLA) robot policies, showing that identical model checkpoints can produce different physical actions due to variations in…
While AI coding tools excel at generating code, they fail to address the critical 70% of software development work involving testing, deployment, and bug fixing. It highlights the "E2E testing gap," w…