PoLAR: Factorizing Extent and Mode in Latent Actions for Robot Policy Learning
Summary
PoLAR introduces a geometrically structured latent action representation in hyperbolic space that separates transition extent from mode, improving robotic policy learning performance.
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Paper page - PoLAR: Factorizing Extent and Mode in Latent Actions for Robot Policy Learning
Source: https://huggingface.co/papers/2606.21139
Abstract
PoLAR introduces a geometrically structured latent action representation in hyperbolic space that separates transition extent from transition mode, improving robotic policy learning performance.
Latent action pretraininglearns representations of visual change from pairs of observations, but existing methods typically encode each transition as a single unstructured representation that entanglestransition extentandtransition mode. We introducePolar Latent ActionswithRadial structure(PoLAR), which imposes a radial-direction structure on latent actions, encouraging radius to encodetransition extentand direction to retaintransition mode. PoLAR usestemporal offsetbetween two observations as a weak proxy fortransition extent, encouraging latent action from observation pairs separated by larger temporal gaps to occupy larger radii. We instantiate this structure inhyperbolic space, whose expanding volume with radius offers a natural fit for more diversetransition modes at larger extents. Across in-task and large-scale pretraining settings, PoLAR improvesdownstream policy performancein simulation and real-world robot experiments, outperforming latent action baselines and strong pretrained VLAs. These results suggest that the geometry of the latent action space is an important design choice for transferringvisual pretrainingto downstream robot policy learning.
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