Autonomous action-mode change in a two-mobile robotic system-S-temperature based online learning

Deals with an autonomous action-mode change in a behavior-based learning of the mobile robotic system, which is composed of two mobile robots consisting of a crawler and a one DOF arm. The task is to carry a long bar horizontally along a compound road keeping the bar-support distance constant. We propose an online learning method-STL (S-temperature based online learning). During the learning process, the robotic system can build up the state categorization in terms of two-dimensional sensory data (support length and support inclination). The robotic system can transit seamlessly between the learning process and the task-achieving process according to the internal state of the robotic system that is controlled by the S-temperature introduced into the STL. We show the effectiveness of STL by hardware robot experiments