Author

Wenchao Ding

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Preprint Sep 2026

TacDyn-WAM: Learning Implicit Tactile Dynamics in a Heterogeneous Visuo-Tactile World Action Model

World action models improve robotic manipulation by conditioning actions on predicted futures, yet existing tactile variants largely inherit video-generation pipelines that reconstruct future tactile observations through iterative denoising. Such prediction can become unreliable under deployment drift: small changes in...

En-Yi Wang, Ming-Xin Wang, Quan Shi et al. · 0 citations

Preprint Aug 2026

StageWAM: Joint-Embedding Stage Prediction for World-Action Models in Robot Manipulation

StageWAM is introduced, which augments a Motus-based World Action Model with Stage-JEPA, a goal-conditioned Joint-Embedding Predictive Architecture (JEPA) predictor, which uses a frozen V-JEPA2 encoder to extract the current-state representation and predicts the latent target of the next inferred stage.

Xiao Liu, Yuguang Yang, Xi Wang et al. · 2 citations · ⚡1

Preprint Aug 2026

Latent Action as Intention Enables Efficient Future Imagination for World Action Models

World action models (WAMs) improve robot control by modeling how observations evolve, but generating future observations at test time incurs substantial latency. Fast-WAM removes this process for efficiency; however, our matched implementations show lower generalization for Fast-WAM than for future-aware alternatives,...

Xiang Li, Yu-Peng Zheng, Song-En Gu et al. · 4 citations · ⚡2