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World Models for Robotic Manipulation: A Survey
Fangyuan Wang, Ziyuan Wang, Guorui Pei +15
Robotic manipulation depends on the ability to anticipate how actions reshape objects, contacts, and scene geometry before execution. Learned world models provide this capability b…
Learning a Kinodynamic Trajectory Manifold for Impact-Aware Compliant Catching of Fast-Moving Objects
Guorui Pei, Mengshi Zhang, Xi Chen +3
Fast catching of free-flying objects is difficult because of short reaction time, impact uncertainty, and kinodynamic constraints. We use reinforcement learning in simulation to co…
Think Proprioceptively: State-Grounded Visual Token Selection for VLA Policies
Fangyuan Wang, Peng Zhou, Jiaming Qi +4
Vision-language-action (VLA) models typically inject proprioception only as a late conditioning signal, preventing robot state from grounding instruction understanding or directing…
Failure-Aware Bimanual Teleoperation via Conservative Value Guided Assistance
Peng Zhou, Zhongxuan Li, Jinsong Wu +7
Teleoperation of high-precision manipulation is con-strained by tight success tolerances and complex contact dy-namics, which make impending failures difficult for human operators…