This study empirically evaluates the perceived realism of six micromovement strategies implemented in two agent embodiments (Humanoid and Dummy).Thirty participants (ages 18-55) recruited via the Gorilla platform viewed randomised 5-second video clips (six micro-movement strategies × two embodiments).After each clip, participants rated five dimensions-Resemblance, Daily-Life Likelihood, Efficiency, Movement Realism, and Overall Agent Realism-on a continuous 0-100 scale.Descriptive statistics for a representative movement (TurnBackward) revealed that the Dummy variant scored lower in Overall Agent Realism than the Humanoid, despite similar Movement Realism ratings.Two-way ANOVAs showed significant main effects of Movement Type on Resemblance, Day-to-day, Efficiency, and Movement Realism but no significant Movement × Embodiment interactions.Agent type significantly influenced only Overall Agent Realism.Mixed Linear Models corroborated that CurveForward and Forward movements positively impacted Resemblance and Day-to-day, while Backward and Strafe had negative effects.Correlation analysis revealed strong positive associations among Resemblance, Day-to-day, Efficiency, and Movement Realism, but weaker links to Overall Agent Realism.These findings validate the proposed micro-movement taxonomy and indicate that movement kinematics drive perceived realism more than agent appearance, guiding future ASA controller design.

In this demonstration a human user interacts with a virtual humanoid character in real-time. Our goal is to create a character that is perceived as imbued with a distinct personality while responding dynamically to inputs from the environment [4] [1]. A hybrid model that comprises continuous and discrete components, firstly, drives the logical behavior of the virtual character moving through states of the interaction, and secondly, continuously updates of the emotional expressions of the virtual character depending on feedback from interactions with the environment. A Rock-Paper-Scissors game scenario is used as framework for the interaction scenario and provides an easy-to-learn and engaging demo environment with minimum conversation.

ABSTRACT Plausible spatial behavior is a key capability that autonomous virtual characters need in order to provide ecologically valid social interactions. However, there is a lack of psychological data on spatial behavior in the larger scale social settings and over extended periods of time. In this paper, we present a social navigation model that aims at generating human‐like spatial behavior for a virtual human in a social setting with group dynamics. We employ an engineering approach by defining a dynamic representation of interest and then using it as the psychometric function that regulates the behavior of the agent. We evaluate our model by means of two test cases that address different aspect of the model and serve as a proof of concept. Our work is a step toward models for generating more plausible social spatial behavior for virtual characters that is based on both internal dynamics and attributes of the social environment. Copyright © 2014 John Wiley & Sons, Ltd.

One of the key challenges of affective computing is to extend the expression of emotions to machines. Research in this field has focused mainly on embodied machines that can reproduce verbal or non-verbal cues such as facial movements and gestures. However, most machines we interact with in our daily life are non-anthropomorphic. For this reason, the question we are addressing in our study is whether it is possible to express emotions or affective states using non-anthropomorphic cues in non-humanoid artifacts. We generated animated motifs using a small set of parameters (color, motion and complexity) and we displayed them on the interactive floor of the experience Induction Machine (XIM), an immersive mixed reality space. We asked the participants to assess the emotions attributed to these abstract visual cues. Our findings suggest that it is not only possible to express affective states, but also to modulate human behavior through non-anthropomorphic and abstract stimuli.

Humans have the ability to use a complex code of non-verbal behavior to communicate their internal states to others. Conversely, the understanding of intentions and emotions of others is a fundamental aspect of human social interaction. In the study presented here we investigate how people perceive the expression of emotional states based on the observation of different styles of locomotion. Our goal is to find a small set of canonical parameters that allow to control a wide range of emotional expressions. We generated different classes of walking behavior by varying the head/torso inclination, the walking speed, and the viewing angle of an animation of a virtual character. 18 subjects rated the observed walking person using the two-dimensional circumplex model of arousal and valence. The results show that, independent of the viewing angle, participants perceived distinct states of arousal and valence. Moreover, we could show that parametrized body posture codes emotional states, irrespective of the contextual influence or facial expressions. These findings suggest that human locomotion transmits basic emotional cues that can be directly related to canonical parameters of different dimensions of the expressive behavior. These findings are important as they allow us to build virtual characters whose emotional expression is recognizable at large distance and during extended periods of time.

By their very nature, virtual reality worlds are spatial. Hence, one of the key requirements for virtual reality applications is the possibility to navigate within the virtual world. Previous studies have shown that the characteristics of the device used for navigation, both in terms of the physical properties, and the interaction logic, has effects on the user's experience. The question we address is how does different navigation modes with the same physical interface affect the user's experience. We selected two modes which differed in the way the user's actions were mapped to movements in the virtual environment. To quantify the difference between the two interaction mappings, spatial memory and the subjective sense of presence were compared. The results of our study showed that interaction mapping affects both, spatial memory and presence. Hence, not only the specific physical device but also the way it is used is important for the user's experience and recollection of it. Additionally, we found a correlation between spatial memory and subjective sense of presence, which indicates that the subjective sense of presence can be estimated from the performance in a spatial memory task.

Rodents are optimal real-world foragers that can smoothly regulate behaviors like homing and exploration combined with more elaborate abilities as food source localization. Here we investigate a robot based model that implements the self-regulatory processes that underly optimal foraging of rodents in unknown environments and is also able to combine it with goal directed behaviors. Behavior is decomposed into minimal homeostatic subsystems that regulate themselves through the local perception/detection of a gradient. On a higher level, the allostatic control orchestrates the interaction of the different homeostatic modules allowing it to dynamically manage the interactions between the desired values of each subsystem to achieve stability on a meta behavioral level. In this case, we show that overall behavioral stability can be achieved. We validate our model by comparing the behavior of both simulated and real robots with that of rodents. Our next step is then to justify gradients as a valid biological assumption by giving a biologically plausible process for generating them from a cognitive map, in this case, a set of approximated hippocampal place cells. We finally formulate path planning (used for goal reaching, e.g. food source localization) in the same context of a gradient map generation that can be then inserted as an additional subsystem of the higher meta level allostatic control.

In principle it appears advantageous for single neurons to perform non-linear operations. Indeed it has been reported that some neurons show signatures of such operations in their electrophysiological response. A particular case in point is the Lobula Giant Movement Detector (LGMD) neuron of the locust, which is reported to locally perform a functional multiplication. Given the wide ramifications of this suggestion with respect to our understanding of neuronal computations, it is essential that this interpretation of the LGMD as a local multiplication unit is thoroughly tested. Here we evaluate an alternative model that tests the hypothesis that the non-linear responses of the LGMD neuron emerge from the interactions of many neurons in the opto-motor processing structure of the locust. We show, by exposing our model to standard LGMD stimulation protocols, that the properties of the LGMD that were seen as a hallmark of local non-linear operations can be explained as emerging from the dynamics of the pre-synaptic network. Moreover, we demonstrate that these properties strongly depend on the details of the synaptic projections from the medulla to the LGMD. From these observations we deduce a number of testable predictions. To assess the real-time properties of our model we applied it to a high-speed robot. These robot results show that our model of the locust opto-motor system is able to reliably stabilize the movement trajectory of the robot and can robustly support collision avoidance. In addition, these behavioural experiments suggest that the emergent non-linear responses of the LGMD neuron enhance the system's collision detection acuity. We show how all reported properties of this neuron are consistently reproduced by this alternative model, and how they emerge from the overall opto-motor processing structure of the locust. Hence, our results propose an alternative view on neuronal computation that emphasizes the network properties as opposed to the local transformations that can be performed by single neurons.

Ada is an entertainment exhibit that is able to interact with many people simultaneously, using a language of light and sound. "She " received 553,700 visitors over 5 months during the Swiss Expo.02 in 2002. In this paper we present the broad motivations, design and technologies behind Ada, and a first overview of the outcomes of the exhibit.

Despite immense progress in neuroscience, we remain restricted in our ability to construct autonomous behaving robots that match the competence of even simple animals. The barriers to the realisation of this goal include: the lack of knowledge of system integration issues, engineering limitations and organisational constraints common to many research laboratories. In this paper we describe our approach to addressing these issues by constructing an artificial organism within the framework of the Ada project - a large-scale public exhibit for the Swiss Expo.02 national exhibition.