Creating a Tendon-Driven Robot That Teaches Itself to Walk with Reinforcement Learning

The robotic limb has an architecture resembling the muscle and tendon structure that powers human and vertebrate movement [1,2]. Tendons connect muscles to bones, making it possible for the biological motors (muscles) to exert force on bones from a distance [3,4]. While tendons have mechanical and structural advantages, a tendon-driven robot is significantly more challenging to control than a traditional robot, where a simple PID controller to control joint angles directly is often sufficient. In a tendon-driven robotic limb, multiple motors may act on a single joint, which means that a given motor may act on multiple joints. As a result, the system is simultaneously nonlinear, over-determined, and under-determined, greatly increasing the control design complexity and calling for a new control design approach.