6 papers · 1 filter
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…
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…
3D Generation for Embodied AI and Robotic Simulation: A Survey
Tianwei Ye, Yifan Mao, Minwen Liao +6
Embodied AI and robotic systems increasingly depend on scalable, diverse, and physically grounded 3D content for simulation-based training and real-world deployment. While 3D gener…
HALO: A Unified Vision-Language-Action Model for Embodied Multimodal Chain-of-Thought Reasoning
Quanxin Shou, Fangqi Zhu, Shawn Chen +9
Vision-Language-Action (VLA) models have shown strong performance in robotic manipulation, but often struggle in long-horizon or out-of-distribution scenarios due to the lack of ex…
WMPO: World Model-based Policy Optimization for Vision-Language-Action Models
Fangqi Zhu, Zhengyang Yan, Zicong Hong +3
Vision-Language-Action (VLA) models have shown strong potential for general-purpose robotic manipulation, but their reliance on expert demonstrations limits their ability to learn…