D-Grasp: Physically Plausible Dynamic Grasp Synthesis for Hand-Object Interactions
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Author / Producer
Date
2022
Publication Type
Conference Paper
ETH Bibliography
yes
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Abstract
We introduce the dynamic grasp synthesis task: given an object with a known 6D pose and a grasp reference, our goal is to generate motions that move the object to a target 6D pose. This is challenging, because it requires reasoning about the complex articulation of the human hand and the intricate physical interaction with the object. We propose a novel method that frames this problem in the reinforcement learning framework and leverages a physics simulation, both to learn and to evaluate such dynamic interactions. A hierarchical approach decomposes the task into low-level grasping and high-level motion synthesis. It can be used to generate novel hand sequences that approach, grasp, and move an object to a desired location, while retaining human-likeness. We show that our approach leads to stable grasps and generates a wide range of motions. Furthermore, even imperfect labels can be corrected by our method to generate dynamic interaction sequences. Video and code are available at: https://eth-ait.github.io/d-grasp/.
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Publication status
published
External links
Editor
Book title
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Journal / series
Volume
Pages / Article No.
20545 - 20554
Publisher
IEEE
Event
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2022)
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Reinforcement learning; motion synthesis; Physics simulation
Organisational unit
03979 - Hilliges, Otmar (ehemalig) / Hilliges, Otmar (former)
Notes
Funding
717054 - Optimization-based End-User Design of Interactive Technologies (EC)