6 papers
Black-Box Adversarial Patch Attacks on VLAs via Ancestor VLM Exploitation
Xiaoyi Pang, Haoyue Feng, Quanxin Shou +3
Vision-Language-Action models (VLAs) are increasingly deployed in safety-critical physical environments, yet their adversarial robustness remains poorly understood. Existing attack…
World Action Learning via Interaction-Centric Spectral Latent Guidance
Zhiming Liu, Yikun Miao, Ying Chen +7
Learning general-purpose robot policies requires large-scale real-world interaction data, yet collecting robot demonstrations remains expensive and difficult to scale. Egocentric v…
SteerQuant: Steering Quantization Error with Action-Guided Scaling in World-Action Models
Yunhan Wang, Haodong Wang, Zhiming Liu +7
World-action models (WAMs) jointly generate future world states and actions through iterative denoising, using shared weights to process heterogeneous semantic streams of video, pr…
OnlineWM: Causality-Aware Active Online Learning for Effective World Modeling
Yikun Miao, Fangqi Zhu, Quanxin Shou +6
Generative world models aim to predict future states conditioned on actions, where action controllability is fundamental for reliable dynamics modeling. While recent efforts levera…
RedFlow: Redirect Failure into Action-level Corrections for Flow-matching VLA Policy
Zhengyang Yan, Junhao Li, Fangqi Zhu +6
Reinforcement learning (RL) can improve Vision-Language-Action (VLA) policies from deployment experience, but reward- and preference-based RL primarily identifies desirable behavio…
Shared Growth of Graph Neural Networks via Prompted Free-direction Knowledge Distillation
Kaituo Feng, Yikun Miao, Changsheng Li +2
Knowledge distillation (KD) has shown to be effective to boost the performance of graph neural networks (GNNs), where the typical objective is to distill knowledge from a deeper te…