Shoe lacing (SL) is a challenging sensorimotor task in daily life and a complex engineering problem in the
shoe-making industry. In this paper, we propose a system for autonomous SL. It contains a mathematical definition of the SL task and searches for the best lacing pattern corresponding to the shoe configuration and the user preferences. We propose a set of action primitives and generate plans of action sequences according to the designed pattern. Our system plans the trajectories based on the perceived position of the eyelets and aglets with an active perception strategy, and deploys the trajectories on a bi-manual robot. Experiments demonstrate that the proposed system can successfully lace 3 different shoes
in different configurations, with a completion rate of 92.0%,
91.6% and 77.5% for 6, 8 and 10-eyelet patterns respectively.
To the best of our knowledge, this is the first demonstration of autonomous SL using a bi-manual robot.