Decoding social intentions in human prehensile actions: Insights from a combined kinematics-fMRI study

Consistent evidence suggests that the way we reach and grasp an object is modulated not only by object properties (e.g., size, shape, texture, fragility and weight), but also by the types of intention driving the action, among which the intention to interact with another agent (i.e., social intention). Action observation studies ascribe the neural substrate of this `intentional' component to the putative mirror neuron (pMNS) and the mentalizing (MS) systems. How social intentions are translated into executed actions, however, has yet to be addressed. We conducted a kinematic and a functional Magnetic Resonance Imaging (fMRI) study considering a reach-to-grasp movement performed towards the same object positioned at the same location but with different intentions: passing it to another person (social condition) or putting it on a concave base (individual condition). Kinematics showed that individual and social intentions are characterized by different profiles, with a slower movement at the level of both the reaching (i.e., arm movement) and the grasping (i.e., hand aperture) components. fMRI results showed that: (i) distinct voxel pattern activity for the social and the individual condition are present within the pMNS and the MS during action execution; (ii) decoding accuracies of regions belonging to the pMNS and the MS are correlated, suggesting that these two systems could interact for the generation of appropriate motor commands. Results are discussed in terms of motor simulation and inferential processes as part of a hierarchical generative model for action intention understanding and generation of appropriate motor commands

Is That a Chair? Imagining Affordances Using Simulations of an Articulated Human Body

For robots to exhibit a high level of intelligence in the real world, they must be able to assess objects for which they have no prior knowledge. Therefore, it is crucial for robots to perceive object affordances by reasoning about physical interactions with the object. In this paper, we propose a novel method to provide robots with an ability to imagine object affordances using physical simulations. The class of chair is chosen here as an initial category of objects to illustrate a more general paradigm. In our method, the robot "imagines" the affordance of an arbitrarily oriented object as a chair by simulating a physical sitting interaction between an articulated human body and the object. This object affordance reasoning is used as a cue for object classification (chair vs non-chair). Moreover, if an object is classified as a chair, the affordance reasoning can also predict the upright pose of the object which allows the sitting interaction to take place. We call this type of poses the functional pose. We demonstrate our method in chair classification on synthetic 3D CAD models. Although our method uses only 30 models for training, it outperforms appearance-based deep learning methods, which require a large amount of training data, when the upright orientation is not assumed to be known a priori. In addition, we showcase that the functional pose predictions of our method align well with human judgments on both synthetic models and real objects scanned by a depth camera.Comment: 7 pages, 6 figures. Accepted to ICRA202

Embodied Robot Models for Interdisciplinary Emotion Research

Due to their complex nature, emotions cannot be properly understood from the perspective of a single discipline. In this paper, I discuss how the use of robots as models is beneficial for interdisciplinary emotion research. Addressing this issue through the lens of my own research, I focus on a critical analysis of embodied robots models of different aspects of emotion, relate them to theories in psychology and neuroscience, and provide representative examples. I discuss concrete ways in which embodied robot models can be used to carry out interdisciplinary emotion research, assessing their contributions: as hypothetical models, and as operational models of specific emotional phenomena, of general emotion principles, and of specific emotion ``dimensions''. I conclude by discussing the advantages of using embodied robot models over other models.Peer reviewe

Applications of Biological Cell Models in Robotics

In this paper I present some of the most representative biological models applied to robotics. In particular, this work represents a survey of some models inspired, or making use of concepts, by gene regulatory networks (GRNs): these networks describe the complex interactions that affect gene expression and, consequently, cell behaviour