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

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arxiv 2505.11865 v1 pith:UDQFSDY3 submitted 2025-05-17 cs.RO cs.CV

Teli Ma , Jia Zheng , Zifan Wang , Ziyao Gao , Jiaming Zhou , Junwei Liang This is my paper
classification cs.ROcs.CV
keywords affordanceglovermanipulationhova-500khumanroboticacrossactionable
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 9 Pith papers

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  3. RoboReact: Agentic Skill Distillation from Generated Egocentric Videos for Generalizable Whole-Body Manipulation

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  4. Afford-VLA: Action-Aligned Visual Planning via Internalized Affordance

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    Afford-VLA internalizes task-conditioned affordance as an explicit visual planning interface within VLA models via learnable <AFF> tokens, achieving SOTA on LIBERO and SimplerEnv benchmarks.

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    PALM improves long-horizon robotic manipulation success by distilling affordance representations for object interaction and predicting within-subtask progress in a VLA model.

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