VLA-Pro: Cross-Task Procedural Memory Transfer for Vision-Language-Action Models
Vision-Language-Action~(VLA) models have shown strong potential for general-purpose robotic manipulation, yet they still struggle to generalize to unseen tasks that necessitate transferring relevant experience across objects, scenes, and action patterns. This paper proposes VLA-Pro, a plug-and-play framework designed to enhance cross-task generalization by storing task-relevant procedural memories at training time and transferring these memories during inference. Specifically, VLA-Pro stores task-specific LoRA adapters as parameterized procedural memories during training. At inference time, VLA-Pro retrieves relevant procedural memories based on the current multi-modal context and dynamic...
| Authors | Shengyu Si, Yuanzhuo Lu, Ruimeng Yang et al. |
|---|---|
| Primary category | cs.RO |
| Published | 28 May 2026 |
| Last updated at source | 28 May 2026 |
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