OrthoSkillVLA: Continual Skill Learning via Gradient-Informed Skill Subspace Adaptation
Research
Source: arXiv cs.ROPublish time unverified
arXiv:2608.19589v1 Announce Type: new Abstract: Pretrained Vision-Language-Action models provide a strong foundation for robot learning, but sequentially adapting them to diverse skills can perturb the representations and velocity mappings used by previous skills, leading to catastrophic forgetting. Architecture-based approaches improve retention by isolating skills but lead to increased inference footprint.