Simple Kinematic Feedback Enhances Autonomous Learning in Bio-Inspired Tendon-Driven Systems.

Abstract

Error feedback is known to improve performance by correcting control signals in response to perturbations. Here we show how adding simple error feedback can also accelerate and robustify autonomous learning in a tendon-driven robot. We have implemented two versions of the General-to-Particular (G2P) autonomous learning algorithm using a tendon-driven leg with two joints and three tendons: one with and one without real-time kinematic feedback. We have performed a rigorous study on the performance of each system, for both simulation and physical implementation cases, over a wide range of tasks. As expected, feedback improved performance in simulation and hardware. However, we see these improvements even in the presence of sensory delays of up to 100 ms and when experiencing substantial contact collisions. Importantly, feedback accelerates learning and enhances G2P's continual refinement of the initial inverse map by providing the system with more relevant data to train on. This allows the system to perform well even after only 60 seconds of initial motor babbling.