6 citations · 6 across the 8 of their papers we have counts for
9 papers
Learning Skills from Historical Action Trajectories: Action Experience Dictionary for World Action Models
Qi Lyu, Jiahua Dong, Hao Shen +9
World Action Models (WAMs) couple visual dynamics prediction with action generation, yet they do not explicitly support the reuse of action experience across manipulation tasks. Fu…
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…
SAB-LVLM: Significance-Aware Binarization for Large Vision-Language Models
Qi Lyu, Jiahua Dong, Baichen Liu +7
Large Vision-Language Models (LVLMs) have achieved remarkable progress in multimodal understanding, yet their enormous parameter scale and cross-modal computation incur substantial…
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…
SeqWalker: Sequential-Horizon Vision-and-Language Navigation with Hierarchical Planning
Zebin Han, Xudong Wang, Baichen Liu +5
Sequential-Horizon Vision-and-Language Navigation (SH-VLN) presents a challenging scenario where agents should sequentially execute multi-task navigation guided by complex, long-ho…
CRISP: Contrastive Residual Injection and Semantic Prompting for Continual Video Instance Segmentation
Baichen Liu, Qi Lyu, Xudong Wang +3
Continual video instance segmentation demands both the plasticity to absorb new object categories and the stability to retain previously learned ones, all while preserving temporal…
Deep Convolutional Neural Networks with Zero-Padding: Feature Extraction and Learning
Zhi Han, Baichen Liu, Shao-Bo Lin +1
This paper studies the performance of deep convolutional neural networks (DCNNs) with zero-padding in feature extraction and learning. After verifying the roles of zero-padding in…
Review helps learn better: Temporal Supervised Knowledge Distillation
Dongwei Wang, Zhi Han, Yanmei Wang +3
Reviewing plays an important role when learning knowledge. The knowledge acquisition at a certain time point may be strongly inspired with the help of previous experience. Thus the…