D-Grasp: Physically Plausible Dynamic Grasp Synthesis for Hand-Object Interactions
| Autor: | Christen, S., Kocabas, M., Aksan, E., Hwangbo, J., Song, J., Hilliges, O. |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Computer Science - Robotics motion synthesis Computer Vision and Pattern Recognition (cs.CV) Reinforcement learning Physics simulation Computer Science - Computer Vision and Pattern Recognition Robotics (cs.RO) Machine Learning (cs.LG) |
| Zdroj: | 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2022) |
| Popis: | 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/. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) ISBN:978-1-6654-6946-3 ISBN:978-1-6654-6947-0 |
| Databáze: | OpenAIRE |
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