Human-robot interface with anticipatory characteristics based on Laban Movement Analysis and Bayesian models

In this work we contribute to the field of human-machine interaction with a system that anticipates human movements using the concept of Laban Movement Analysis (LMA). The implementation uses a Bayesian model for learning and classification and results are presented for the application to online gesture recognition. The merging of assistive robotics and socially interactive robotics has recently led to the definition of socially assistive robotics. What is necessary and we found still missing are socially interactive robots with a higher level cognitive system which analyzes deeply the observed human movement. In this article we provide a framework for cognitive processes to be implemented in human-machine-interfaces based on nowadays technologies. We present LMA as a concept that helps to identify useful low-level features, defines a framework of mid-level descriptors for movement-properties and helps to develop a classifier of expressive actions. Our interface anticipates a performed action observed from a stream of monocular camera images by using a Bayesian framework. With this work we define the required qualities and characteristics of future embodied agents in terms of social interaction with humans. This article searches for human qualities like anticipation and empathy and presents possible ways towards implementation in the cognitive system of a social robot. We present results through its embodiment in the social robot 'Nicole' in the context of a person performing gestures and 'Nicole' reacting by means of audio output and robot movement.