Robots are increasingly envisaged as our future cohabitants. However, while considerable progress has been made in recent years in terms of their technological realization, the ability of robots to inter-act with humans in an intuitive and social way is still quite limited. An important challenge for social robotics is to determine how to design robots that can perceive the user’s needs, feelings, and intentions, and adapt to users over a broad range of cognitive abilities. It is conceivable that if robots (...) were able to adequately demonstrate this, humans would eventually accept them as social companions. We argue that the best way to achieve this is using a systematic experimental approach based on behavioral and physiological neuroscience methods such as motion/eye tracking, electroencephalography (EEG), or functional near-infrared spectroscopy (fNIRS) embedded in interactive human-robot paradigms. This approach requires understanding how humans interact with each other, how they perform tasks together and how they develop feelings of social connection over time, and using these insights to formulate design principles that make social robots attuned to the workings of the human brain. In this review, we put forward the argument that the likelihood of artificial agents being perceived as social companions can be increased by designing them in a way that they are perceived as intentional agents that activate areas in the human brain involved in social-cognitive processing. We first review literature related to social-cognitive mechanisms involved in human-human interactions, and high-light the importance of perceiving others as intentional agents to activate these brain areas. We then discuss how attribution of intentionality can positively affect human-robot interaction by (a) fostering feelings of social connection, empathy and prosociality, and by (b) enhancing performance on joint human-robot tasks. Lastly, we describe circumstances under which attribution of intentionality to robot agents might be disadvantageous, and discuss challenges associated with designing social robots that are inspired by neuroscientific principles. (shrink)

Understanding the goals of others is fundamental for any kind of interpersonal interaction and collaboration. From a neurocognitive perspective, intention understanding has been proposed to depend on an involvement of the observer’s motor system in the prediction of the observed actions. An open question is if a similar understanding of the goal mediated by motor resonance can occur not only between humans, but also for humanoid robots. In this study we investigated whether goal-oriented robotic actions can induce motor resonance by (...) measuring the appearance of anticipatory gaze shifts to the goal during action observation. Our results indicate a similar implicit processing of humans’ and robots’ actions and propose to use anticipatory gaze behaviour as a tool for the evaluation of human-robot interactions. Keywords: Humanoid robot; motor resonance; anticipation; proactive gaze; action understanding. (shrink)

This article presents results from a multidisciplinary research project on the integration and transfer of language knowledge into robots as an empirical paradigm for the study of language development in both humans and humanoid robots. Within the framework of human linguistic and cognitive development, we focus on how three central types of learning interact and co-develop: individual learning about one's own embodiment and the environment, social learning (learning from others), and learning of linguistic capability. Our primary concern is how these (...) capabilities can scaffold each other's development in a continuous feedback cycle as their interactions yield increasingly sophisticated competencies in the agent's capacity to interact with others and manipulate its world. Experimental results are summarized in relation to milestones in human linguistic and cognitive development and show that the mutual scaffolding of social learning, individual learning, and linguistic capabilities creates the context, conditions, and requisites for learning in each domain. Challenges and insights identified as a result of this research program are discussed with regard to possible and actual contributions to cognitive science and language ontogeny. In conclusion, directions for future work are suggested that continue to develop this approach toward an integrated framework for understanding these mutually scaffolding processes as a basis for language development in humans and robots. (shrink)

In 1665 Athanasius Kircher published the treatise Mundus Subterraneus, to ex-plore the complex relationships between the visible forms of the landscape and the reasons that produce them. Kircher was peer of Claude Lorrain, who gave a critical contribution in founding landscape as an artistic genre, making indistinguishable its existence as a real place and its representation as a picture. Compared to Kircher, Lorrain had the greatest influence on Western landscape culture. Yet today, thanks to scholars and landscape architecture practitioners, Kircher’s (...) intuition seems effective to describe the contemporary idea of landscape as a performative field, encompassing all living forms, soils and waters, and intangible substances working together, impossible to understand just by the investigation of their visible forms. Today landscape exists because it is perceived and because it works: Lorrain and Kircher have finally met. (shrink)

Action perception and recognition are core abilities fundamental for human social interaction. A parieto-frontal network (the mirror neuron system) matches visually presented biological motion information onto observers' motor representations. This process of matching the actions of others onto our own sensorimotor repertoire is thought to be important for action recognition, providing a non-mediated “motor perception” based on a bidirectional flow of information along the mirror parieto-frontal circuits. State-of-the-art machine learning strategies for hand action identification have shown better performances when sensorimotor (...) data, as opposed to visual information only, are available during learning. As speech is a particular type of action (with acoustic targets), it is expected to activate a mirror neuron mechanism. Indeed, in speech perception, motor centers have been shown to be causally involved in the discrimination of speech sounds. In this paper, we review recent neurophysiological and machine learning-based studies showing (a) the specific contribution of the motor system to speech perception and (b) that automatic phone recognition is significantly improved when motor data are used during training of classifiers (as opposed to learning from purely auditory data). (shrink)

In agreement with the target article, we would like to point out a few aspects related to embodiment which further support the position of biorobotics. We argue that, especially when complex systems are considered, modeling through a physical implementation can provide hints to comprehend the whole picture behind the specific set of experimental data.

This research compared job involvement level between congruence personality types and work environmental model. Holland has categorized personality orientation in to six types: Realistic (R), Investigative (I), Artistic (A), Social (S), Enterprising (E), and Conventional (C). Subject of this study was administration employee from X University. The background of the education of the respondents at least junior high school level and have one year experience in X University. One-way Anova calculation shows that year work experience of job involvement between employee (...) with different personality types.  . (shrink)

This paper reports about our investigation on action understanding in the brain. We review recent results of the neurophysiology of the mirror system in the monkey. Based on these observations we propose a model of this brain system which is responsible for action recognition. The link between object affordances and action understanding is considered. To support our hypothesis we describe two experiments where some aspects of the model have been implemented. In the first experiment an action recognition system is trained (...) by using data recorded from human movements. In the second experiment, the model is partially implemented on a humanoid robot which learns to mimic simple actions performed by a human subject on different objects. These experiments show that motor information can have a significant role in action interpretation and that a mirror-like representation can be developed autonomously as a result of the interaction between an individual and the environment. (shrink)