Author

Hui Shen

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#artificial intelligence Preprint Sep 2026

HIL-UMI: Bringing Human-in-the-Loop Post-Training of Vision-Language-Action Models to Universal Manipulation Interface

Large-scale vision-language-action (VLA) models provide powerful priors for robot manipulation, yet adapting them to a specific deployment remains challenging. Supervised fine-tuning (SFT) on task-specific demonstrations provides a step toward deployment, but faces two persistent limitations: static data provide limite...

Zimu Han, Yi-Ming Zeng, Ji-Yao Zhang et al. · 0 citations

#artificial intelligence Review Sep 2026

Harbor Adapters and Harbor-Index: Infrastructure and a Curated Meta-Dataset for Large-Scale Agentic Evaluation

Harbor Adapters are developed that port more than 80 benchmarks to evaluate arbitrary agents, and validated through rigorous code review and parity experiments, and Harbor-Index is introduced, a curated set of 82 difficult, diverse, and high-quality tasks spanning 29 benchmarks to support more reliable and comprehensiv...

Lin Shi, Hao-Wei Lin, Zi-Xuan Zhu et al. · 1 citation

Jul 2026

Test-Time Scaling for World Action Models via Zero-Shot Geometric Evaluation

Test-time scaling improves foundation-model inference by spending additional computation, but robot control requires deciding whether extra compute is useful before executing an action. World Action Models (WAMs) make this decision natural: each rollout exposes both an action chunk and predicted future observations. We...

Zesen Zhao, Minkyoung Cho, Hui Shen et al. · arXiv.org · 6 citations · ⚡2