Dexterous Functional Grasping
Ananye Agarwal, Shagun Uppal, Kenneth Shaw, Deepak Pathak

TL;DR
This paper presents a modular approach combining affordance matching and eigengrasp-based low-level control to enable dexterous, functional grasping of in-the-wild objects, outperforming baselines and human teleoperation.
Contribution
It introduces a novel eigengrasp application to reduce RL search space, improving stability and realism in dexterous grasping of diverse objects.
Findings
Eigengrasp-based control outperforms baselines in simulation.
The approach surpasses hardcoded grasping in real-world tests.
Matches or exceeds human teleoperator performance.
Abstract
While there have been significant strides in dexterous manipulation, most of it is limited to benchmark tasks like in-hand reorientation which are of limited utility in the real world. The main benefit of dexterous hands over two-fingered ones is their ability to pickup tools and other objects (including thin ones) and grasp them firmly to apply force. However, this task requires both a complex understanding of functional affordances as well as precise low-level control. While prior work obtains affordances from human data this approach doesn't scale to low-level control. Similarly, simulation training cannot give the robot an understanding of real-world semantics. In this paper, we aim to combine the best of both worlds to accomplish functional grasping for in-the-wild objects. We use a modular approach. First, affordances are obtained by matching corresponding regions of different…
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Taxonomy
TopicsRobot Manipulation and Learning · Robotic Locomotion and Control · Motor Control and Adaptation
