4 papers
LEEVLA: Seeing What Matters in Latent Environment Evolution for Vision-Language-Action
Qi Lyu, Baicheng Liu, Xudong Wang +3
Vision-language-action (VLA) models aim to map multimodal inputs to robot actions. However, most existing approaches struggle to cover complex dynamic scenarios due to treating all…
Lifelong Embodied Navigation Learning
Xudong Wang, Jiahua Dong, Baichen Liu +3
Embodied navigation agents powered by large language models have shown strong performance on individual tasks but struggle to continually acquire new navigation skills, which suffe…
Lifelong Language-Conditioned Robotic Manipulation Learning
Xudong Wang, Zebin Han, Zhiyu Liu +5
Traditional language-conditioned manipulation agent sequential adaptation to new manipulation skills leads to catastrophic forgetting of old skills, limiting dynamic scene practica…
Rehearsal-free Federated Domain-incremental Learning
Rui Sun, Haoran Duan, Jiahua Dong +3
We introduce a rehearsal-free federated domain incremental learning framework, RefFiL, based on a global prompt-sharing paradigm to alleviate catastrophic forgetting challenges in…