RynnUnify 16B model unifies embodied understanding, planning, and control
Authors report a 9.75-point Rynn-VP gain over Cosmos 3 Nano and 46.7% average success on three real-robot tasks.
Stories per day · 30 days
Relatedworld-models3robotics2droid1embodied-ai1hierarchical-planning1in-context-learning1jepa1physics-simulation1reinforcement-learning1rynnunify1
4 stories, newest first · one card per story
Stories tagged #visual-planning
PhysiCalWorld calibrates world models from in-context physics clips
One reusable descriptor token, trained only with latent prediction loss, improves rollout accuracy and visual planning.
H-JEPA: End-to-End Learning of Hierarchical World Models for Visual Planning
H-JEPA trains hierarchical action-conditioned world models, raising Visual AntMaze planning success from 18% to 73%.
World Action Modeling with Progressive Visual Planning
ProWAM predicts actions and sparse visual sub-goals, posting new robotics scores and 70% real-world success.