Automated Robot Behavior Recognition

Abstract

Automated recognition of the behavior of robots is increasingly needed in a variety of tasks, as we develop more autonomous robots and general information processing agents. For example, in environments with multiple autonomous robots, a robot may need to make decisions based on the behavior of the other robots. As another interesting example, an intelligent narrator agent observing a robot will need to automatically identify the robot’s behaviors. In this paper, we introduce a novel framework for using Hidden Markov Models (HMMs) to represent and recognize strategic behaviors of robotic agents. We first introduce and characterize the perceived signal in terms of behavioral-relevant state features. We then show how several HMMs capture different defined robot behaviors Finally we present the HMM-based recognition algorithm which orchestrates and selects the appropriate HMMs in real time. We use the multi-robot robotic soccer domain as the substrate of our empirical validation, both in simulation and using real robots. Robots will then adapt their behaviors as a function of the autonomously recognized behavior of the other agents, either teammates or opponents.