This paper introduces the concept and use of an Interaction History Architecture for use on a mobile domestic robot and analyses the limitations of this configuration. The interaction history architecture builds upon Shannon information theory and has been previously used in a humanoid robot to learn basic children’s games. Previous work has shown that experience spaces can be highly flexible when used for learning. In this paper we outline and experiment designed to test the abilities of the architecture and how it can be used with classic clicker style training to teach domestic robots simple tasks. It then presents results from an experiment exploring these capabilities as well as the limitation found therein.

This paper presents an ongoing collaboration to develop a perceptual anchoring framework which creates and maintains the symbol-percept links concerning household objects. The paper presents an approach to non-trivialize the symbol system using ontologies and allow for HRI via enabling queries about objects properties, their affordances, and their perceptual characteristics as viewed from the robot (e.g. last seen). This position paper describes in brief the objective of creating a long term perceptual anchoring framework for HRI and outlines the preliminary work done this far.

In order to successfully interact with multiple humans in social situations, an intelligent robot should have the ability to track multi-humans, and understand their motion intentions. We formalize this problem as a hidden Markov model, and estimate the posterior densities by particle filtering over sets approach. Our approach avoids directly performing observation-to-target association by defining a set as a joint state. The human identification problem is then solved in an expectation-maximization way. We evaluate the effectiveness of our approach by both benchamark test and real robot experiments.

The two applications under development The EMOTE project (http://www.emote-project.eu/) is are a Treasure Hunt exercise designed to teach mapreading working towards the development of an empathic robot tutor skills, and a multi-player game Enercities-2 designed to be used with the 11-14 group and a multi-touch table to teach aspects of sustainable urban development.

Recent psychological experiments intend to show that social intentions can be read from the recording of motor actions (Becchio, Sartori, and Castiello 2010; Ferri et al. 2011). At the center of the debate is the hypothesis that the motor system is (Blackemore and Decety 2001), or is not (Jacob and Jeannerod 2005) used to recognize social intentions, with a potential openning to a bottom-up understanding of social behavior, agentivity and theory of mind. In (Becchio et al. 2007), the authors proposed to record the arm's trajectories during episodes of a "pick and place" task with a motor vs social outcome. The results provided evidence for differences in motor patterning depending on the social context and intention, but where not yet a direct evidence of the involvement of the motor system in recognizing social intention. In (Becchio, Sartori, and Castiello 2010; Ferri et al. 2011), the authors show how social affordances can change the movement parametrization with the hypothesis that a same action linked to a social context may involve an increase of the index of difficulty.

A paradigm of case-supported principle-based behavior (CPB) is proposed to help ensure ethical behavior of autonomous machines. We argue that ethically significant behavior of autonomous systems should be guided by explicit ethical principles determined through a consensus of ethicists. Such a consensus is likely to emerge in many areas in which autonomous systems are apt to be deployed and for the actions they are liable to undertake, as we are more likely to agree on how machines ought to treat us than on how human beings ought to treat one another. Given such a consensus, particular cases of ethical dilemmas where ethicists agree on the ethically relevant features and the right course of action can be used to help discover principles needed for ethical guidance of the behavior of autonomous systems. Such principles help ensure the ethical behavior of complex and dynamic systems and further serve as a basis for justification of their actions as well as a control abstraction for managing unanticipated behavior. The requirements, methods, implementation, and evaluation components of the CPB paradigm are detailed.

A social robot should be able to understand human’s profile (i.e., human’s emotions and personality), so as to make the robot able to behave appropriately to the multimodal interaction context. This research addresses the online recognition of emotions based on a new fuzzy-based methodology. It also focuses on investigating how could a match between the human’s and the robot’s personalities influence interaction. Furthermore, it studies the automatic generation of head-arm metaphoric gestures under different emotional states based on the prosodic cues of the interacting human. The conducted experiments have been validated with NAO robot from Aldebaran Robotics and ALICE robot from Hanson Robotics.

Eye contact is a crucial behaviour in human communication and therefore an essencial feature in human-robot interaction. A study regarding the development of an eye behaviour model for a robotic tutor in a task-oriented environment is presented, along with a description of how our proposed model is being used to implement an autonomous robot in the EMOTE project.

The field of socially assistive robotics (SAR) aims to build robots that help people through social interaction. Human social interaction involves complex systems of behavior, and modeling these systems is one goal of SAR. Nonverbal behaviors, such as eye gaze and gesture, are particularly amenable to modeling through machine learning because the effects of the system—the nonverbal behaviors themselves—are inherently observable. Uncovering the underlying model that defines those behaviors would allow socially assistive robots to become better interaction partners. Our research investigates how people use nonverbal behaviors in tutoring applications. We use data from human-human interactions to build a model of nonverbal behaviors using supervised machine learning. This model can both predict the context of observed behaviors and generate appropriate nonverbal behaviors.

In signal analysis and synthesis, linear approximation theory considers a linear decomposition of any given signal in a set of atoms, collected into a so-called dictionary. Relevant sparse representations are obtained by relaxing the orthogonality condition of the atoms, yielding overcomplete dictionaries with an extended number of atoms. More generally than the linear decomposition, overcomplete kernel dictionaries provide an elegant nonlinear extension by defining the atoms through a mapping kernel function (e.g., the gaussian kernel). Models based on such kernel dictionaries are used in neural networks, gaussian processes and online learning with kernels. The quality of an overcomplete dictionary is evaluated with a diversity measure the distance, the approximation, the coherence and the Babel measures. In this paper, we develop a framework to examine overcomplete kernel dictionaries with the entropy from information theory. Indeed, a higher value of the entropy is associated to a further uniform spread of the atoms over the space. For each of the aforementioned diversity measures, we derive lower bounds on the entropy. Several definitions of the entropy are examined, with an extensive analysis in both the input space and the mapped feature space.