Geometry-Aware Motion Latents for Learning Robust Manipulation Policies
Learning motion latents for robotic manipulation heavily relies on extracting motion patterns from visual sequences, yet effective action abstractions require understanding three-dimensional geometric transformations. Here, we introduce GeoMoLa (Geometry-Aware Motion Latents), which learns discrete motion latent codes by predicting how point clouds evolve during manipulation rather than reconstructing visual observations. This four-dimensional objective -- spatial geometry changing through time -- forces latent representations to encode actual physical motion rather than appearance patterns. GeoMoLa achieves state-of-the-art performance using only single-view RGB-D input, while existing m...
| Authors | Yunchao Zhang, Yijia Weng, Ruizhe Liu et al. |
|---|---|
| Primary category | cs.RO |
| Published | 6 July 2026 |
| Last updated at source | 6 July 2026 |
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