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cs.RO2026
ACG-WAM: World-Action Modeling via Action-Conditioned Geometric Latent Prediction
Jiangtao Liu, Zishang Xiang, Yage He +4
World action models jointly learn visual predictionand robot actions, providing a way to use observations ofscene evolution for policy learning. Their video and actionlosses, howev…
cs.RO2026
Language-Forcing: Advancing Humanoid Loco-Manipulation via Semantic Alignment of Whole-Body World Action Models
Yanjun Li, Jiarui Ye, Weiyu Guo +7
World Action Models (WAMs) jointly predict robot actions and future observations. However, their visual prediction objectives do not explicitly distinguish task-relevant transition…
cs.RO2026
Optimization-based Safe Trajectory Planning for Autonomous Ground Vehicle in Multi-Floor Scenarios
Zishang Xiang, Runda Zhang, Runqi Chai +3
The development of trajectory planning strategies for autonomous ground vehicles (AGVs) represents a prevailing research interest within the domain of intelligent transportation sy…