An approach for behavior discovery using clustering of dynamics

As robots enter more complex application domains and start to interact autonomously with their surroundings and with humans, it becomes essential that they can efficiently represent and interpret their streams of sensor data and model the behavior of objects in their environment. To do this automatically and without the need for extensive a priori models of the environment, these systems have to be able to autonomously discover the different dynamic behaviors of entities in their environment as well as to predict points at which objects´ behaviors and interactions change. This paper presents a technique to simultaneously learn to identify segmentation points and to extract a discrete set of models of the behaviors of objects from a stream of sensor observations. The approach presented here uses unsupervised learning techniques to learn these models in the form of representative sensor signatures which, once acquired, are used to interpret the robot´s observations and to represent the actual sensor data more compactly as a sequence of behaviors. To enable the system to simultaneously learn to segment the continuous data stream and to build appropriate models for the data segments, a set of similarity metrics for sensor streams is derived and used in an expectation maximization algorithm. This algorithm alternates between segmenting the sensor data based on the existing behavior models and clustering the data segments to derive better models, thus iteratively improving both the quality of the segmentation and of the behavior models. To illustrate the approach, experiments are performed using simulated observations derived from different types of sensors which observe the dynamic interactions of objects.