What Matters for Latent Actions in Robot Learning
Latent Action Models (LAMs) have emerged as a promising paradigm for enabling robot learning to leverage large-scale unlabeled videos through latent actions that serve as compact surrogates for physical actions. Despite rapid progress, research on LAM remains highly fragmented, with existing methods evaluating different design choices in isolation under inconsistent experimental settings, making it difficult to identify the factors that truly determine downstream robotic manipulation performance. In this work, we present the first comprehensive empirical study of latent action learning for robotic manipulation. We unify representative LAM methods within a common autoencoding framework and...
| Authors | Xizhou Bu, Qingda Hu, Lei Zhou et al. |
|---|---|
| Primary category | cs.RO |
| Published | 20 August 2026 |
| Last updated at source | 26 September 2026 |
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