IEEE Robotics and Automation Letters

Combining multiple sensory inputs has been a big hurdle for developing physical AI robots. Recent research at Tohoku University solves this problem using a brand new approach.

Walking and running is notoriously difficult to recreate in robots. Now, a group of researchers has overcome some of these challenges by creating an innovative method that employs central pattern generators - neural circuits located in the spinal cord that generate rhythmic patterns of muscle activity - with deep reinforcement learning. The method not only imitates walking and running motions but also generates movements for frequencies where motion data is absent, enables smooth transition movements from walking to running, and allows for adapting to environments with unstable surfaces.

Human motor control has always been efficient at executing complex movements naturally, efficiently, and without much thought involved. This is because of the existence of motor synergy in the central nervous system (CNS). Motor synergy allows the CNS to use a smaller set of variables to control a large group of muscles; thereby simplifying the control over coordinated and complex movements.