ModAR predicts robot futures one modality at a time; 30M model beats 6B video model
k7agar · x · 2026-09-17
World-action models usually imagine the future in RGB, but pixels spend capacity on details irrelevant to robot policies. ModAR instead predicts the future one modality at a time — DINO features, point tracks, depth — with each prediction informing the next. Trained from scratch, its 30.1M-parameter model outperforms a 6B video-model-initialized baseline finetuned on the task.
More from Embodied
- New probe tests 10 vision foundation models on persistent spatial state across 4 levels — liuziwei7 · 2026-11-02
- Wearing Whoop and Apple Watch on a single dual-slot band — tinyfool · 2026-11-02
- REA: async brain-cerebellum runtime for robots where policies never wait on the LLM — Pengfei Zhou · 2026-11-02
- Robot Combat Sports Coming to Japan with a Permanent Arena in Tokyo — cixliv · 2026-11-02
- REK robot fighting in the cage: humanoid bot in boxing gloves draws a crowd — broodsugar · 2026-11-02
- iPhone Duo: a folding iPhone mod impresses, but its hinge raises crackgate concerns — rudrank · 2026-11-02