Can we extend the power of world models beyond just online model-based learning? Absolutely! We believe the true potential of world models lies in enabling agents to reason at test time. Introducing DINO-WM: World Models on Pre-trained Visual Features for Zero-shot Planning.

New paper! We show that by using keypoint-based image representation, robot policies become robust to different object types and background changes. We call this method Prescriptive Point Priors for robot Policies or P3-PO in short. Full project is here: point-priors.github.io

Modern policy architectures are unnecessarily complex. In our #NeurIPS2024 project called BAKU, we focus on what really matters for good policy learning. BAKU is modular, language-conditioned, compatible with multiple sensor streams & action multi-modality, and importantly fully open-source!