Learning Action Priors for Cross-embodiment Robot Manipulation
Most Vision-Language-Action (VLA) models build on a Vision-Language Model (VLM) backbone by attaching an action module and optimizing the full policy jointly. This design inherits strong visual and linguistic priors from the VLM, but leaves the action module to learn physical motion almost from scratch. As a result, the policy lacks an explicit motion prior, forcing early optimization to simultaneously discover temporal action dynamics and cross-modal alignment, a challenge further amplified in cross-embodiment settings. In this work, we propose to pretrain the action module with motion priors before cross-modal VLA alignment. Specifically, we introduce a two-stage training framework that...
| Authors | Dong Jing, Tianqi Zhang, Jiaqi Liu et al. |
|---|---|
| Primary category | cs.RO |
| Published | 24 June 2026 |
| Last updated at source | 24 June 2026 |
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