Unifying Policy Learning and State Prediction through Spatial Language Modeling
Learning how actions change scene geometry can provide complementary supervision for goal-directed manipulation. We introduce Spatial Language Modeling, which represents scene contours, goals, action targets, and future states with a shared vocabulary of discrete coordinates and semantic tokens. A task-specific grammar organizes these elements into spatial sequences, allowing one autoregressive Transformer to learn action generation and action-conditioned state prediction through a common next-token objective. We train the model from scratch using random-play transition pretraining followed by joint action and state training on expert demonstrations. During pretraining, recorded action co...
| Authors | Minye Wu, Zehao Wang, Tinne Tuytelaars |
|---|---|
| Primary category | cs.RO |
| Published | 8 October 2026 |
| Last updated at source | 8 October 2026 |
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