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cs.RO2026

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…

cs.RO2026

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…

cs.RO2026

TwinRL: Digital Twin-Driven Reinforcement Learning for Real-World Robotic Manipulation

Qinwen Xu, Jiaming Liu, Rui Zhou +11

Despite strong generalization capabilities, Vision-Language-Action (VLA) models remain constrained by the high cost of expert demonstrations and limited real-world interaction. Whi…

cs.RO2026

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…

cs.RO2026

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…