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
GIFT (Guided Intermediate Feature Training), an architecture-flexible framework for learning intermediate features that translates these structures into training-time constraints through geometry alignment, affordance prediction, and goal-region reconstruction, establishes learning functionally structured intermediate...
Yu-Peng Zheng, Xiang Li, Song-En Gu et al.· 2 citations
VisCo is a training-efficient self-compression framework that reuses the pretrained VLM itself as an intrinsic compressor that compresses visual information using a small set of memory tokens and transfers hierarchical information from encoding to decoding.
Yupeng Zheng, Kai Zou, Bin Liu et al.· arXiv.org· 0 citations
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,...
A latent memory pool is constructed that stores failure cases along with their structure scene representations and expert trajectory labels, and a dedicated Retrieve Model that decouples static road structure and dynamic agent interactions to enable structurally grounded retrieval is designed.
Zebin Xing, Yu-Peng Zheng, Qiang-Yu Chen et al.· 0 citations
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