5 citations · 17 across the 41 of their papers we have counts for
13 papers · 2 filters
From bits to atoms: A survey of cross-layer safety in Embodied AI
Tuo Feng, Yue Zhang, Shiji Zhou +35
Embodied Artificial Intelligence (AI) introduces cyber-physical risks because digital failures and attacks can cause physical harm. This survey organizes embodied AI safety through…
Token-World: World Modeling in Vision-Language Model Token Space for Robot Manipulation
Chuyao Fu, Xiaowei Chi, Yuhan Rui +14
A common approach to world-model simulation for vision-language-action (VLA) systems is to predict future RGB observations and then re-encode them into policy inputs, introducing a…
OpenWAM: An Open, Modular Exploration Towards Systematic World-Action Model Pretraining
Yuran Wang, Siqiao Huang, Mingleyang Li +21
World-Action Models inherit world knowledge from video-generative priors, and channel it into executable control signals through embodied experience. Existing systems, however, are…
Data Pyramid for Embodied Manipulation: A Survey
Yifan Ye, Yankai Fu, Yaoxu Lv +26
Multimodal foundation models learned to see and to speak by consuming the whole internet. Embodied agents admit no such shortcut, since they require data that couple observations w…
MV-WAM: Manifold-Aware World Action Model with Value Augmentation
Jintao Chen, Peidong Jia, Qingpo Wuwu +13
Achieving robust and generalizable manipulation across diverse environments remains a fundamental challenge in embodied robotics. Recent world action models achieve strong in-domai…
WAM-RL: World-Action Model Reinforcement Learning with Reconstruction Rewards and Online Video SFT
Zezhong Qian, Xiaowei Chi, Yu Qi +3
Recent World-Action (WA) models demonstrate strong generalization ability and data efficiency, but they typically rely on expert trajectories for training. This reliance limits the…
SafeDojo: Safe Reinforcement Learning for VLA via Interactive World Model
Kai Tang, Peidong Jia, Zhong Chu +16
Safe control is a prerequisite for real-world embodied intelligence, for which safe reinforcement learning has emerged as a promising paradigm. However, existing safe reinforcement…
Efficient-WAM: A 1B-Parameter World-Action Model with Low-Cost Future Imagination
Jiajun Li, Tiecheng Guo, Yifan Ye +10
World-Action Models (WAMs) have emerged as a promising paradigm for embodied control by coupling future visual prediction with action generation. However, many existing WAMs rely o…
Dream-Tac: A Unified Tactile World Action Model for Contact-Rich Robot Manipulation
Yunfan Lou, Yifan Ye, Yankai Fu +7
World action models inherit the predictive capability of world models, enabling action generation to be guided by anticipated future observations. However, they rely primarily on v…
ProDrive: Proactive Planning for Autonomous Driving via Ego-Environment Co-Evolution
Chuyao Fu, Shengzhe Gan, Zhuoli Ouyang +5
End-to-end autonomous driving planners typically generate trajectories from current observations alone. However, real-world driving is highly dynamic, and such reactive planning ca…
Mask World Model: Predicting What Matters for Robust Robot Policy Learning
Yunfan Lou, Xiaowei Chi, Xiaojie Zhang +9
World models derived from large-scale video generative pre-training have emerged as a promising paradigm for generalist robot policy learning. However, standard approaches often fo…
TC-IDM: Grounding Video Generation for Executable Zero-shot Robot Motion
Weishi Mi, Yong Bao, Xiaowei Chi +7
The vision-language-action (VLA) paradigm has enabled powerful robotic control by leveraging vision-language models, but its reliance on large-scale, high-quality robot data limits…
Wow, wo, val! A Comprehensive Embodied World Model Evaluation Turing Test
Chun-Kai Fan, Xiaowei Chi, Xiaozhu Ju +18
As world models gain momentum in Embodied AI, an increasing number of works explore using video foundation models as predictive world models for downstream embodied tasks like 3D p…