Robust dexterous hand control strategy cascading bare hand pose estimation and joint jitter suppression

Abstract

Vision-based dexterous hand control via human hand intuition has great potential in improving control naturalness and immersion, which further achieves better dexterity and generalization. However, challenges still exist in robust control, which is affected by environmental issues including estimation fluctuations and human hand physiological tremor. In this paper, we develop a novel dexterous hand control strategy cascading bare hand pose estimation and joint jitter suppression to enhance controlling robustness. The bare hand pose estimation network utilizes CNNs, ASCS-RL and a biologic-awared refinement module. A zero-delay low pass filter with threshold is then introduced to suppress joint jitters.

Mingqi Chen

Postdoctoral Researcher