arXiv Computer Vision
Aug 21

OrthoSkillVLA: Continual Skill Learning via Gradient-Informed Skill Subspace Adaptation

arXiv:2608. 19589v1 Announce Type: cross 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.

By Jiaqi Wang, Zhou Fang, Qiongfeng Shi, Yi Zhou
arXiv AI
Sep 28

Prompt-Based Continual Compositional Zero-Shot Learning

The paper introduces PromptCCZSL, a framework that enables vision‑language models to continually learn new attributes, objects, and their unique compositions while avoiding forgetting. It uses a frozen VLM backbone with prompt‑based techniques, recency‑weighted multi‑teacher distillation, and several loss functions (CAL, OPL, IDL) to maintain prior knowledge and promote diverse, distinct embeddings. Experiments on UT‑Zappos and C‑GQA show significant performance gains over existing VLM‑based and non‑VLM baselines, establishing a new benchmark for continual compositional zero‑shot learning.

By Sauda Maryam, Sara Nadeem, Faisal Qureshi, Mohsen Ali