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 demonstrations for specific objects. We propose leveraging human grasp information extracted from web images, which capture natural and functional hand-object interactions (HOI). Using a pretrained 3D reconstruction model, we recover 3D human HOI meshes from RGB images. To train on these noisy HOI data, we propose to us
The proliferation of web imagery and advancements in 3D reconstruction models coincide, enabling innovative approaches to training robotic manipulation. There is a growing demand for more dexterous and functional robotic capabilities beyond simple power grasps.
This development moves robotic manipulation beyond limited, in-domain demonstrations by leveraging a vast, real-world dataset of human-object interactions, accelerating progress towards general-purpose robot dexterity. It addresses a key bottleneck in robotics: the ability to perform nuanced functional grasps.
Robot grip learning can now be massively scaled using readily available human interaction data from the web, rather than relying solely on expensive and limited lab demonstrations. This broadens the scope of tasks robots can learn to perform functionally.
- · Robotics companies
- · Logistics and manufacturing automation
- · AI researchers in computer vision
- · Companies reliant on highly specialized, single-task robotic systems
Robots will be able to perform a wider range of dexterous manipulation tasks with greater adaptability.
The cost and time required to program versatile robot manipulators will significantly decrease, accelerating automation across various industries.
This could lead to a more rapid integration of humanoid robots into complex environments requiring nuanced human-like interaction.
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Read at arXiv cs.LG