Acquisition of Stand-up Behavior by a Real Robot Using Hierarchical Reinforcement Learning

Abstract

In this paper, we propose a hierarchical reinforcement learning architecture that realizes practical learning speed in real hardware control tasks. In order to enable learning in a practical number of trials, we introduce a low-dimensional representation of the state of the robot for higher-level planning. The upper level learns a discrete sequence of sub-goals in a low-dimensional state space for achieving the main goal of the task. The lower-level modules learn local trajectories in the original high-dimensional state space to achieve the sub-goal specified by the upper level. We applied the hierarchical architecture to a three-link, two-joint robot for the task of learning to stand up by trial and error. The upper-level learning was implemented by Q-learning, while the lower-level learning was implemented by a continuous actor–critic method. The robot successfully learned to stand up within 750 trials in simulation and then in an additional 170 trials using real hardware. The effects of the setting of the search steps in the upper level and the use of a supplementary reward for achieving sub-goals are also tested in simulation.