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 this https URL .

使用隐式函数学习生成灵巧手运动的抓取计划，结合条件变分自动编码器和 3D 人体演示进行生成建模，该模型名为连续抓取函数（CGF）。通过在模拟器进行不断采样，并选择成功的抓取计划转移到真实机器人手上，与之前的规划算法相比，CGF 更高效，成功率也显著提高。