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Web2Grasp learns functional robot grasps from web images

By PulseAugur Editorial · [1 sources] ·

Researchers have developed Web2Grasp, a novel method for teaching robot hands to grasp objects functionally by learning from human interactions captured in web images. This approach extracts 3D meshes of hand-object interactions from RGB images and employs an interaction-centric model along with geometry-based filtering and physical simulation to refine grasp data. The system achieved a 75.8% success rate on web-dataset objects in simulation and demonstrated a 77.5% success rate with real-world robotic hands, including challenging objects like syringes and knives. AI

IMPACT Enables more versatile and functional robotic manipulation by leveraging readily available web data.

RANK_REASON This is a research paper detailing a new method for robotic grasping. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Web2Grasp learns functional robot grasps from web images

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This is a research paper detailing a new method for robotic grasping. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Hongyi Chen, Yunchao Yao, Yufei Ye, Zhixuan Xu, Homanga Bharadhwaj, Jiashun Wang, Arthur Jakobsson, Ruihan Zhao, Shubham Tulsiani, Zackory Erickson, Jeffrey Ichnowski ·

    Web2Grasp: Learning Functional Grasps from Web Images of Hand-Object Interactions

    arXiv:2505.05517v3 Announce Type: replace-cross Abstract: Functional grasping is essential for enabling dexterous multi-finger robot hands to manipulate objects effectively. Prior work largely focuses on power grasps, which only involve holding an object, or relies on in-domain d…

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