ArtiGrasp: Physically Plausible Synthesis of Bi-Manual Dexterous Grasping and Articulation

Hui Zhang, Sammy Christen, Zicong Fan, Luocheng Zheng, Jemin Hwangbo, Jie Song, Otmar Hilliges

Track: Paper Submission Track

Keywords: motion synthesis; hand-object interaction; physics simulation

TL;DR: We present a method to synthesize physically plausible bi-manual manipulation. Our method can generate motion sequences such as grasping and relocating an object with one or two hands, and opening it to a target articulation angle.

Abstract: We present ArtiGrasp, a novel method to synthesize bi-manual hand-object interactions that include grasping and articulation. This task is challenging due to the diversity of the global wrist motions and the precise finger control that is necessary to articulate objects. ArtiGrasp leverages reinforcement learning and physics simulations to train a policy that controls the global and local hand pose. Our framework unifies grasping and articulation within a single policy guided by a single hand pose reference. Moreover, to facilitate the training of the precise finger control required for articulation, we present a learning curriculum with increasing difficulty. It starts with single-hand manipulation of stationary objects and continues with multi-agent training including both hands and non-stationary objects. To evaluate our method, we introduce Dynamic Object Grasping and Articulation, a task that aims to bring an object into a target articulated pose. This task requires grasping, relocation, and articulation. We show our method’s efficacy towards this task.

Submission Number: 180