GLOVER++: Unleashing the Potential of Affordance Learning from Human Behaviors for Robotic Manipulation

Teli Ma, Jia Zheng, Zifan Wang, Ziyao Gao, Jiaming Zhou, Junwei Liang

Keywords: Actionable Affordance, Affordance Transfer, Vision-Language Model, Human Demonstrations, Robotic Manipulation

Abstract:

Learning manipulation skills from human demonstration videos offers a promising path toward generalizable and interpretable robotic intelligence—particularly through the lens of *actionable affordances*. However, transferring such knowledge remains challenging due to: 1) a lack of large-scale datasets with precise affordance annotations, and 2) insufficient exploration of affordances in diverse manipulation contexts. To address these gaps, we introduce **HOVA-500K**, a large-scale, affordance-annotated dataset comprising 500,000 images across 1,726 object categories and 675 actions. We also release a standardized benchmarking suite for multi-modal affordance reasoning. Built upon HOVA-500K, we present **GLOVER++**, a *global-to-local* affordance training framework that effectively transfers actionable affordance knowledge from human demonstrations to downstream open-vocabulary reasoning tasks. GLOVER++ achieves state-of-the-art results on the HOVA-500K benchmark and demonstrates strong generalization across diverse downstream robotic manipulation tasks.
By explicitly modeling actionable affordances, GLOVER++ facilitates robust transfer across scenes, modalities, and tasks. We hope that HOVA-500K and the GLOVER++ framework will serve as valuable resources for bridging the gap between human demonstrations and robotic manipulation capabilities. We will release our dataset, code and models.

Submission Number: 174