Modeling What Changes: Sparse, Residual World Models for Object-Centric Manipulation
Monolithic world models predict the entire next state at every step, spending capacity re-predicting the static majority of a scene and injecting error into it. We ask whether explicitly modeling change (a per-object change gate plus a residual delta head that perturbs only the objects the gate flags) is a more effective and interpretable bias for physical prediction and control. On a MuJoCo tabletop pushing benchmark scaling from 3 to 8 objects, the sparse/residual model predicts next-state poses 2.5 to 4.6 times more accurately than a dense multilayer perceptron at 8.6 to 11.1 times fewer parameters, sustains change-detection F1 of 0.80 to 0.87 where the dense baseline is degenerate, tr...
| Authors | Param Thakkar, Parsika Paresh Shah, Manisha Sushant Gote |
|---|---|
| Primary category | cs.RO |
| Published | 2 September 2026 |
| Last updated at source | 2 September 2026 |
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