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
Vision-Language-Action (VLA) models are increasingly evaluated across multiple simulation benchmarks, yet adding each benchmark to an evaluation pipeline requires resolving incompatible dependencies, matching underspecified evaluation protocols, and reverse-engineering undocumented preprocessing. This burden scales wit...
Suhwan Choi, YUN-OO Lee, Yubeen Park et al.· arXiv.org· 8 citations
This work introduces HumanCLAW, an evaluation framework that decouples action decision-making from low-level execution in a vision-language model (VLM), and builds HumanCLAW-Bench, a database of 1,218 long-horizon, egocentric find-navigate-interact episodes across 41 indoor scenes.
Siyao Li, Jiawei Gu, Shuai Liu et al.· 1 citation
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