A new race of artifacts comes equipped with behavioral properties. Those properties transmute the very nature of the object, granting it a life of its own and a special status that stems from the psychological attributions humans naturally produce when confronted by autonomous movements. This article examines what makes behavioral objects special in terms of the psychological properties they evoke in an observer. We look into the notion of behavior and evaluate to what extent the concept of anthropomorphism is a valid construct when considering the behavior of artificial objects. Based on recent research in cognitive psychology, we propose a framework to conceptualize the way people infer psychological attributes from movement, and the way it applies to behavioral objects.

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When faced with the need of implementing a decentralized behavior for a group of collaborating robots, strategies inspired from swarm intelligence often avoid considering the human operator, granting the swarm with full autonomy. However, field missions require at least to share the output of the swarm to the operator. Unfortunately, little is known about the users’ perception of group behavior and dynamics, and there is no clear optimal interaction modality for swarms. In this paper, we focus on the movement of the swarm to convey information to a user: we believe that the interpretation of artificial states based on groups motion can lead to promising natural interaction modalities. We implement a grammar of decentralized control algorithms to explore their expressivity. We define the expressivity of a movement as a metric to measure how natural, readable, or easily understandable it may appear. We then correlate expressivity with the control parameters for the distributed behavior of the swarm. A first user study confirms the relationship between inter-robot distance, temporal and spatial synchronicity, and the perceived expressivity of the robotic system. We follow up with a small group of users tasked with the design of expressive motion sequences to convey internal states using our grammar of algorithms. We comment on their design choices and we assess the interpretation performance by a larger group of users. We show that some of the internal states were perceived as designed and discuss the parameters influencing the performance.

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This article raises the issue of collaboration between the field of dance and that of robotics. Dance notation systems are developed in the choreographic field as a method to describe and record human movements. Translating movements into symbols is of particular concern for computer science and robotics, which are interested in generating anthropomorphic motions in robots or animated avatars. We examine different research projects on the use of Laban notation for this purpose, and present our own attempt at generating dance movements in a humanoid robot from a Laban score. We discuss the interests and difficulties related to the use of a dance notation system in a robotic context.