PoLAR: Factorizing Extent and Mode in Latent Actions for Robot Policy Learning

arXiv:2606.21139v1 Announce Type: new Abstract: Latent action pretraining learns representations of visual change from pairs of observations, but existing methods typically encode each transition as a single unstructured representation that entangles transition extent and transition mode. We introduce Polar Latent Actions with Radial structure (PoLAR), which imposes a radial-direction structure on latent actions, encouraging radius to encode transition extent and direction to retain transition m...