Manipulation policies must know which objects matter and where they are, yet the pretrained backbones that current robot foundation models build on, from language in vision-language-action models (VLAs) to video generation in world-action models (WAMs), do not directly require this metric grounding, leaving it to be le...
Ge-Hao Zhang, Weikai Huang, S. Shailesh et al.· 0 citations
Latent Interface Training (LIT), a framework-agnostic two-stage strategy that first establishes a spatial-goal-conditioned action prior without images, then constrains visual conditioning through a pose-supervised latent interface, improves overall LIBERO-Plus success while preserving or improving average LIBERO succes...
Jian-Man Lin, S. Shailesh, Zhong-Yi Luo et al.· 1 citation
Despite the impressive visuomotor capabilities enabled by Vision-Language-Action (VLA) models, their performance often degrades on challenging and out-of-domain tasks. Recent test-time steering and scaling methods improve performance without extensive data collection and retraining, but action samples often remain conc...
Derek Ming Siang Tan, S. Shailesh, Srikrishna Iyer et al.· arXiv.org· 0 citations
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