Social Perception of Pedestrians and Virtual Agents Using Movement Features

In many tasks such as navigation in a shared space, humans explicitly or implicitly estimate social information related to the emotions, dominance, and friendliness of other humans around them. This social perception is critical in predicting others’ motions or actions and deciding how to interact with them. Therefore, modeling social perception is an important problem for robotics, autonomous vehicle navigation, and VR and AR applications. In this thesis, we present novel, data-driven models for the social perception of pedestrians and virtual agents based on their movement cues, including gaits, gestures, gazing, and trajectories. We use deep learning techniques (e.g., LSTMs) along with biomechanics to compute the gait features and combine them with local motion models to compute the trajectory features. Furthermore, we compute the gesture and gaze representations using psychological characteristics. We describe novel mappings between these computed gaits, gestures, gazing, and trajectory features and the various components (emotions, dominance, friendliness, approachability, and deception) of social perception. Our resulting data-driven models can identify the dominance, deception, and emotion of pedestrians from videos with an accuracy of more than 80%. We also release new datasets to evaluate these methods. We apply our data-driven models to socially-aware robot navigation and the navigation of autonomous vehicles among pedestrians. Our method generates robot movement based on pedestrians’ dominance levels, resulting in higher rapport and comfort. We also apply our data-driven models to simulate virtual agents with desired emotions, dominance, and friendliness. We perform user studies and show that our data-driven models significantly increase the user’s sense of social presence in VR and AR environments compared to the baseline methods.Doctor of Philosoph

Trust model of privacy-concerned, emotionally-aware agents in a cooperative logistics problem

In this paper we propose a trust model to be used into a hypothetical mixed environment where humans and unmanned vehicles cooperate. We address the inclusion of emotions inside a trust model in a coherent way to the practical approaches to the current psychology theories. The most innovative contribution is how privacy issues play a role in the cooperation decisions of the emotional trust model. Both, emotions and trust have been cognitively modeled and managed with the Beliefs, Desires and Intentions (BDI) paradigm into autonomous agents implemented in GAML (the programming language of GAMA agent platform) that communicates using the IEEE FIPA standard. The trusting behaviour of these emotional agents is tested in a cooperative logistics problem where: agents have to move objects to destinations and some of the objects and places have privacy issues. The execution of simulations of this logistic problem shows how emotions and trust contribute to improve the performance of agents in terms of both, time savings and privacy protectio

Choreographic and Somatic Approaches for the Development of Expressive Robotic Systems

As robotic systems are moved out of factory work cells into human-facing environments questions of choreography become central to their design, placement, and application. With a human viewer or counterpart present, a system will automatically be interpreted within context, style of movement, and form factor by human beings as animate elements of their environment. The interpretation by this human counterpart is critical to the success of the system's integration: knobs on the system need to make sense to a human counterpart; an artificial agent should have a way of notifying a human counterpart of a change in system state, possibly through motion profiles; and the motion of a human counterpart may have important contextual clues for task completion. Thus, professional choreographers, dance practitioners, and movement analysts are critical to research in robotics. They have design methods for movement that align with human audience perception, can identify simplified features of movement for human-robot interaction goals, and have detailed knowledge of the capacity of human movement. This article provides approaches employed by one research lab, specific impacts on technical and artistic projects within, and principles that may guide future such work. The background section reports on choreography, somatic perspectives, improvisation, the Laban/Bartenieff Movement System, and robotics. From this context methods including embodied exercises, writing prompts, and community building activities have been developed to facilitate interdisciplinary research. The results of this work is presented as an overview of a smattering of projects in areas like high-level motion planning, software development for rapid prototyping of movement, artistic output, and user studies that help understand how people interpret movement. Finally, guiding principles for other groups to adopt are posited.Comment: Under review at MDPI Arts Special Issue "The Machine as Artist (for the 21st Century)" http://www.mdpi.com/journal/arts/special_issues/Machine_Artis