Iliopoulos, Konstantinos and Bellotto, Nicola and Mavridis, Nikolaos (2014) From sequence to trajectory and vice versa: solving the inverse QTC problem and coping with realworld trajectories. In: AAAI Spring Symposium: "Qualitative Representations for Robots", 2426 March 2014, Stanford University, CA, USA.
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Item Type:  Conference or Workshop contribution (Presentation) 

Item Status:  Live Archive 
Abstract
Spatial interactions between agents carry information of high value to human observers, as exemplified by the highlevel interpretations that humans make when watching the Heider and Simmel movie, or other such videos which just contain motions of simple objects, such as points, lines and triangles. However, not all the information contained in a pair of continuous trajectories is important; and thus the need for qualitative descriptions of interaction trajectories arises. Towards that purpose, Qualitative Trajectory Calculus (QTC) has been proposed in (Van de Weghe, 2004). However, the original definition of QTC handles uncorrupted continuoustime trajectories, while realworld signals are noisy and sampled in discretetime. Also, although QTC presents a method for transforming trajectories to qualitative descriptions, the inverse problem has not yet been studied. Thus, in this paper, after discussing several aspects of the transition from ideal QTC to discretetime noisy QTC, we introduce a novel algorithm for solving the QTC inverse problem; i.e. transforming qualitative descriptions to archetypal trajectories that satisfy them. Both of these problems are particularly important for the successful application of qualitative trajectory calculus to HumanRobot Interaction.
Keywords:  Robotics, Qualitative spatial representation, Qualitative trajectory calculus, Humanrobot spatial interaction 

Subjects:  H Engineering > H670 Robotics and Cybernetics G Mathematical and Computer Sciences > G700 Artificial Intelligence G Mathematical and Computer Sciences > G400 Computer Science 
Divisions:  College of Science > School of Computer Science 
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ID Code:  13519 
Deposited On:  12 Mar 2014 13:24 
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