Using a controller based on reinforcement learning for a passive dynamic walking robot

One of the difficulties with passive dynamic walking is the stability of walking. In our robot, small uneven or tilted parts of the floor disturb the locomotion and must be dealt with by the feedback controller of the hip actuation mechanism. This paper presents a solution to the problem in the form of controller that is based on reinforcement learning. The control mechanism is studied using a simulation model that is based on a mechanical prototype of passive dynamic walking robot with a conventional feedback controller. The successful walking results of our simulated walking robot with a controller based on reinforcement learning showed that in addition to the prime principle of our mechanical prototype, new possibilities such as optimization towards various goals like maximum speed and minimal cost of transport, and adaptation to unknown situations can be quickly found