We propose to learn to generate grasping motion for manipulation with a dexterous hand using implicit functions. With continuous time inputs, the model can generate a continuous and smooth grasping plan. We name the proposed model Continuous Grasping Function (CGF). CGF is learned via generative modeling with a Conditional Variational Autoencoder using 3D human demonstrations. We will first convert the large-scale human-object interaction trajectories to robot demonstrations via motion retargeting, and then use these demonstrations to train CGF. During inference, we perform sampling with CGF to generate different grasping plans in the simulator and select the successful ones to transfer to the real robot. By training on diverse human data, our CGF allows generalization to manipulate multiple objects. Compared to previous planning algorithms, CGF is more efficient and achieves significant improvement on success rate when transferred to grasping with the real Allegro Hand. Our project page is available at https://jianglongye.com/cgf .

翻译：我们提出使用隐式函数学习用Dexterous Hand进行操作的抓取动作。使用连续的时间输入，模型可以生成连续平滑的抓取计划。我们将所提出的模型称为Continuous Grasping Function (CGF)。使用三维人体演示，通过条件变分自动编码器进行生成建模学习CGF。我们首先通过动作重新定位将大规模的人-物交互轨迹转换为机器人演示，然后用这些演示来训练CGF。在推理过程中，我们使用CGF进行采样，在模拟器中生成不同的抓取计划，并选择成功的计划来转移到真实机器人上。通过在多种物体上进行训练，我们的CGF可以推广到操作多种物体。与以前的规划算法相比，CGF更加高效，并在转移到使用真实Allegro Hand的抓取时取得了显着改进。我们的项目页面可在 https://jianglongye.com/cgf 上获得。