What Matters for Latent Actions in Robot Learning
Classification
View four quadrants- Major category
- VLA
- Quadrant
- Not assigned
- Architecture
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- Prediction paradigm
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- Subcategories
- Latent action pretraining
- Source review status
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Category review. An inverse/forward model learns video-derived latent-action supervision before language-conditioned robot policy training; deployment predicts physical actions without world-model rollouts. Joint code/action prediction is not joint future/action prediction. Reading evidence
Contribution
This empirical study asks which video-derived latent actions improve robot policies. It compares modeling paradigms, regularization, and action integration under a shared training framework. Raw-frame LAPO remains competitive, but preferred dimensionality and action heads depend on the evaluation. Its strongest deployment evidence is improved Franka manipulation after latent-action tuning of a VLM backbone; the forward predictor supplies training supervision rather than an inference-time planner.
Abstract
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Affiliations
Fudan University; Tsinghua University; Shenzhen University of Advanced Technology; Sichuan University; Suzhou Evans Intelligent Technology Co., Ltd.; Morphi Intelligence Technology Co., Ltd.; Wuhan University; Xiaomi EV