Gaze-Based Human-Robot Interaction by the Brunswick Model

We present a new paradigm for human-robot interaction based on social signal processing, and in particular on the Brunswick model. Originally, the Brunswick model copes with face-to-face dyadic interaction, assuming that the interactants are communicating through a continuous exchange of non verbal social signals, in addition to the spoken messages. Social signals have to be interpreted, thanks to a proper recognition phase that considers visual and audio information. The Brunswick model allows to quantitatively evaluate the quality of the interaction using statistical tools which measure how effective is the recognition phase. In this paper we cast this theory when one of the interactants is a robot; in this case, the recognition phase performed by the robot and the human have to be revised w.r.t. the original model. The model is applied to Berrick, a recent open-source low-cost robotic head platform, where the gazing is the social signal to be considered

Spectral Bisection Tree Guided Deep Adaptive Exemplar Autoencoder for Unsupervised Domain Adaptation

Learning with limited labeled data is always a challenge in AI problems, and one of promising ways is transferring well-established source domain knowledge to the target domain, i.e., domain adaptation. In this paper, we extend the deep representation learning to domain adaptation scenario, and propose a novel deep model called ``Deep Adaptive Exemplar AutoEncoder (DAE$^2$)''. Different from conventional denoising autoencoders using corrupted inputs, we assign semantics to the input-output pairs of the autoencoders, which allow us to gradually extract discriminant features layer by layer. To this end, first, we build a spectral bisection tree to generate source-target data compositions as the training pairs fed to autoencoders. Second, a low-rank coding regularizer is imposed to ensure the transferability of the learned hidden layer. Finally, a supervised layer is added on top to transform learned representations into discriminant features. The problem above can be solved iteratively in an EM fashion of learning. Extensive experiments on domain adaptation tasks including object, handwritten digits, and text data classifications demonstrate the effectiveness of the proposed method

Psychological Engagement in Choice and Judgment Under Risk and Uncertainty

Theories of choice and judgment assume that agents behave rationally, choose the higher expected value option, and evaluate the choice consistently (Expected Utility Theory, Von Neumann, & Morgenstern, 1947). However, researchers in decision-making showed that human behaviour is different in choice and judgement tasks (Slovic & Lichtenstein, 1968; 1971; 1973). In this research, we propose that psychological engagement and control deprivation predict behavioural inconsistencies and utilitarian performance with judgment and choice. Moreover, we explore the influences of engagement and control deprivation on agent’s behaviours, while manipulating content of utility (Kusev et al., 2011, Hertwig & Gigerenzer 1999, Tversky & Khaneman, 1996) and decision reward (Kusev et al, 2013, Shafir et al., 2002)

Task Allocation in Foraging Robot Swarms:The Role of Information Sharing

Autonomous task allocation is a desirable feature of robot swarms that collect and deliver items in scenarios where congestion, caused by accumulated items or robots, can temporarily interfere with swarm behaviour. In such settings, self-regulation of workforce can prevent unnecessary energy consumption. We explore two types of self-regulation: non-social, where robots become idle upon experiencing congestion, and social, where robots broadcast information about congestion to their team mates in order to socially inhibit foraging. We show that while both types of self-regulation can lead to improved energy efficiency and increase the amount of resource collected, the speed with which information about congestion flows through a swarm affects the scalability of these algorithms