PoLAR：分解機器人策略學習中潛在動作的程度與模式

摘要

潛在動作預訓練透過觀察對學習視覺變化的表徵，但現有方法通常將每個轉變編碼為單一非結構化表示，使得轉變程度與轉變模式相互糾纏。我們提出具有徑向結構的極坐標潛在動作（PoLAR），為潛在動作施加徑向方向結構，促使半徑編碼轉變程度，方向保留轉變模式。PoLAR利用兩個觀察之間的時間偏移作為轉變程度的弱代理，鼓勵時間間隔較大的觀察對所產生的潛在動作佔據較大半徑。我們在雙曲空間中實現此結構，其半徑隨體積擴展的特性，自然而然地適合容納更大程度下更多元的轉變模式。在任務內與大規模預訓練場景中，PoLAR提升了下游策略在模擬與真實機器人實驗中的表現，優於潛在動作基線及強大的預訓練VLA模型。這些結果表明，潛在動作空間的幾何結構是將視覺預訓練遷移至下游機器人策略學習的重要設計選擇。

English

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 mode. PoLAR uses temporal offset between two observations as a weak proxy for transition extent, encouraging latent action from observation pairs separated by larger temporal gaps to occupy larger radii. We instantiate this structure in hyperbolic space, whose expanding volume with radius offers a natural fit for more diverse transition modes at larger extents. Across in-task and large-scale pretraining settings, PoLAR improves downstream policy performance in 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 transferring visual pretraining to downstream robot policy learning.