@inproceedings{Christen:CVPR:2022,
  title = {{D-Grasp}: Physically Plausible Dynamic Grasp Synthesis for Hand-Object Interactions},
  booktitle = {2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2022)},
  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.},
  pages = {20545--20554},
  publisher = {IEEE},
  address = {Piscataway, NJ},
  month = jun,
  year = {2022},
  author = {Christen, Sammy and Kocabas, Muhammed and Aksan, Emre and Hwangbo, Jemin and Song, Jie and Hilliges, Otmar},
  doi = {10.1109/CVPR52688.2022.01992},
  month_numeric = {6}
}