Author

Ke-Fei Zhu

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Preprint Oct 2026

ResMerge: Residual Policy Learning and Merging for Continual Adaptation of Pre-trained Robot Policies

Continual adaptation to new tasks is essential for robots deployed in the real world, but adapting pre-trained policies remains challenging due to parameter growth and the need for continual demonstration collection. Parameter-Efficient Continual Fine-Tuning (PECFT) isolates task-specific updates, but adapter-per-task...

Ke-Fei Zhu, Ling-Yun Xu, Yue Xin et al. · 0 citations

Preprint Sep 2026

OpenWAM: An Open, Modular Exploration Towards Systematic World-Action Model Pretraining

World-Action Models inherit world knowledge from video-generative priors, and channel it into executable control signals through embodied experience. Existing systems, however, are monolithic: the generative backbone, visual representation, architecture, information flow, inference procedure, and training data are tigh...

Yu-Ran Wang, Siqiao Huang, Ming-Lei Li et al. · 38 citations · ⚡4

#artificial intelligence Preprint Oct 2026

RAP-WAM: Learning World-Action Models through Predictive Rehearsal and Action Perturbations

Recent world action models draw on video generation priors to jointly predict future observations and robot actions. Adapting these priors to control requires determining how future predictions should guide policy learning. RAP-WAM combines predictive Rehearsal and Action Perturbations (RAP) with future-supervised quer...

Yuan-Peng He, Ce-Yao Zhang, Fang Li et al. · 0 citations