Robust Tracking of Human Hand Postures for Robot Teaching

To enable the creation of manual interaction databases, aiding the replication of dexterous capabilities with anthropomorphic robot hands by utilizing information about how humans perform complex manipulation tasks, requires the capability to record and analyze large amounts of manual interaction sequences. For this goal we have studied and compared three mappings from captured human hand motion data to a simulated model, which allow for robust and accurate real-time hand posture tracking. We evaluate the effectiveness of these mappings and discuss their pros and cons in various real-world scenarios. The first method is based on data glove data and aims for direct gaging of hand joints. The other two methods utilize a VICON motion tracking system which monitors markers placed on all finger segments. Here we compare two approaches: a direct computation of hand postures from angles between adjacent markers and an iterative inverse kinematics approach to optimally reproduce fingertip positions. For a quantitative evaluation, we employ a "calibration objects" technique to obtain a reliable ground truth of task-relevant hand posture data.