Reinforcement learning of dynamic motor sequence: learning to stand up

We propose a learning method for implementing human-like sequential movements in robots. As an example of dynamic sequential movement, we consider the "stand-up" task for a two-joint, three-link robot. In contrast to the case of steady walking or standing, the desired trajectory for such a transient behavior is very difficult to derive. The goal of the task is to find a path that links a lying state to an upright state under the constraints of the system dynamics. The geometry of the robot is such that there is no static solution; the robot has to stand up dynamically utilizing the momentum of its body. We use reinforcement learning, in particular, a continuous time and state temporal difference (TD) learning method. For successful results, we use 1) an efficient method of value function approximation in a high-dimensional state space, and 2) a hierarchical architecture which divides a large state space into a few smaller pieces.