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arXiv · 2112.03028

D-Grasp: Physically Plausible Dynamic Grasp Synthesis for Hand-Object Interactions

Sammy Christen·Muhammed Kocabas·Emre Aksan·Jemin Hwangbo·Jie Song·Otmar Hilliges
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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.

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Sammy Christen, Muhammed Kocabas, Emre Aksan, Jemin Hwangbo, Jie Song, Otmar Hilliges. 2021-12-01. D-Grasp: Physically Plausible Dynamic Grasp Synthesis for Hand-Object Interactions. https://arxiv.org/abs/2112.03028

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