Models of motion patterns for mobile robotic systems

Human robot interaction is an emerging area of research with many challenges. Knowledge about human behaviors could lead to more effective and efficient interactions of a robot in populated environments. This paper presents a probabilistic framework for the learning and representation of human motion patterns in an office environment. It is based on the observation that most human trajectories are not random. Instead people plan trajectories based on many considerations, such as social rules and path length. Motion patterns are learned using an incrementally growing Sampled Hidden Markov Model. This model has a number of interesting properties which can be of use in many applications. For example, the learned knowledge can be used to predict motion, infer social rules, thus improve a robot's operation and its interaction with people in a populated space. The proposed learning method is extensively validated in real world experiments.