Fine Teleoperation of an Anthropomorphic Robotic Hand by Combining Joint-Cartesian Space

Mapping human hand motion to an anthropomorphic robotic hand is crucial for dexterous hand teleoperation. However, numerous challenges remain due to the high Degrees-of-Freedom (DoF) of human hands and mismatches in sizes and kinematic configurations between human and robotic hands. In recent years, various mapping methods have been proposed to address these challenges, particularly hybrid mapping, which combines the advantages of Cartesian and joint mapping. Although many studies have focused on hybrid mapping, they primarily conducted simulation experiments and did not consider the substantial gaps between simulated and real hands. In this paper, we design a hybrid mapping framework combining proposed mapping methods and extend it from simulation to reality. Specifically, to bridge the gap between human and robotic hands, we propose a novel Cartesian mapping method based on an auxiliary frame to compensate for errors. In the experiment, we utilize a highly under-actuated exoskeleton to capture the movement of the human hand and apply the hybrid mapping method in fine dual-arm teleoperation tasks. The experimental results demonstrate that the hybrid mapping method based on our proposed Cartesian mapping method exhibits outstanding performance in the fine and dexterous teleoperation tasks.