Decoupling the Declarative from the Procedural in Vision-Language-Action Models
Deploying generalist robotic agents in the real world requires transferable skills. Specifically, a policy trained to clone a behavior from object-specific demonstrations must generalize beyond that object, otherwise data collection requirements become intractable. Recently, fine-tuning of pre-trained billion-parameter Vision-Language Models (VLMs), initially on large-scale robot datasets and then on fewer scenario-specific demonstrations, has emerged as the predominant paradigm for designing Vision-Language-Action (VLA) models. While these policies achieve state-of-the-art manipulation performance in-distribution, they remain brittle to minor spatial, semantic, and task variations. In th...
| Authors | Nikolaos Tsagkas, Andreas Sochopoulos, Chris Xiaoxuan Lu et al. |
|---|---|
| Primary category | cs.RO |
| Published | 19 June 2026 |
| Last updated at source | 9 October 2026 |
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