Embodied Gesture Processing: Motor-Based Integration of Perception and Action in Social Artificial Agents

A close coupling of perception and action processes is assumed to play an important role in basic capabilities of social interaction, such as guiding attention and observation of others’ behavior, coordinating the form and functions of behavior, or grounding the understanding of others’ behavior in one’s own experiences. In the attempt to endow artificial embodied agents with similar abilities, we present a probabilistic model for the integration of perception and generation of hand-arm gestures via a hierarchy of shared motor representations, allowing for combined bottom-up and top-down processing. Results from human-agent interactions are reported demonstrating the model’s performance in learning, observation, imitation, and generation of gestures

Gesture recognition using a marionette model and dynamic bayesian networks (dbns

Abstract. This paper presents a framework for gesture recognition by modeling a system based on Dynamic Bayesian Networks (DBNs) from a Marionette point of view. To incorporate human qualities like anticipation and empathy inside the perception system of a social robot remains, so far an open issue. It is our goal to search for ways of implementation and test the feasibility. Towards this end we started the development of the guide robot ’Nicole ’ equipped with a monocular camera and an inertial sensor to observe its environment. The context of interaction is a person performing gestures and ’Nicole ’ reacting by means of audio output and motion. In this paper we present a solution to the gesture recognition task based on Dynamic Bayesian Network (DBN). We show that using a DBN is a human-like concept of recognizing gestures that encompass the quality of anticipation through the concept of prediction and update. A novel approach is used by incorporating a marionette model in the DBN as a trade-off between simple constant acceleration models and complex articulated models.