Joachim de Greeff

Towards retro-projected robot faces: An alternative to mechatronic and android faces

This paper presents a new implementation of a robot face using retro-projection of a video stream onto a semitransparent facial mask. The technology is contrasted against mechatronic robot faces, of which Kismet is a typical example, and android robot faces, as used on the Ishiguro robots. The paper highlights the strengths of Retro-projected Animated Faces (RAF) technology (with cost, flexibility and robustness being notably strong) and discusses potential developments.

A study of a retro-projected robotic face and its effectiveness for gaze reading by humans

Reading gaze direction is important in human-robot interactions as it supports, among others, joint attention and non-linguistic interaction. While most previous work focuses on implementing gaze direction reading on the robot, little is known about how the human partner in a human-robot interaction is able to read gaze direction from a robot. The purpose of this paper is twofold: (1) to introduce a new technology to implement robotic face using retro-projected animated faces and (2) to test how well this technology supports gaze reading by humans. We briefly discuss the robot design and discuss parameters influencing the ability to read gaze direction. We present an experiment assessing the users ability to read gaze direction for a selection of different robotic face designs, using an actual human face as baseline. Results indicate that it is hard to recreate human-human interaction performance. If the robot face is implemented as a semi sphere, performance is worst. While robot faces having a human-like physiognomy and, perhaps surprisingly, video projected on a flat screen perform equally well and seem to suggest that these are the good candidates to implement joint attention in HRI.

Child-Robot Interaction: Perspectives and Challenges

Child-Robot Interaction (cHRI) is a promising point of entry into the rich challenge that social HRI is. Starting from three years of experiences gained in a cHRI research project, this paper offers a view on the opportunities offered by letting robots interact with children rather than with adults and having the interaction in real-world circumstances rather than lab settings. It identifies the main challenges which face the field of cHRI: the technical challenges, while tremendous, might be overcome by moving away from the classical perspective of seeing social cognition as residing inside an agent, to seeing social cognition as a continuous and self-correcting interaction between two agents.

Human-Robot Interaction in Concept Acquisition: a computational model

This paper presents a discussion and simulation results which support the case for interaction during the acquisition of conceptual knowledge. Taking a developmental perspective, we first review a number of relevant insights on word-meaning acquisition in young children and specifically focus on concept learning supported by linguistic input. We present a computational model implementing a number of acquisition strategies, which enable a learning agent to actively steer the learning process. This is contrasted to a one way learning method, where the learner does not actively influence the learning experience. We present results demonstrating how dyadic interaction between a teacher and learner may result in a better acquisition of concepts.

TRADR Project: Long-Term Human-Robot Teaming for Robot Assisted Disaster Response

This paper describes the project TRADR: Long-Term Human-Robot Teaming for Robot Assisted Disaster Response. Experience shows that any incident serious enough to require robot involvement will most likely involve a sequence of sorties over several hours, days and even months. TRADR focuses on the challenges that thus arise for the persistence of environment models, multi-robot action models, and human-robot teaming, in order to allow incremental capability improvement over the duration of a mission. TRADR applies a user centric design approach to disaster response robotics, with use cases involving the response to a medium to large scale industrial accident by teams consisting of human rescuers and several robots (both ground and airborne). This paper describes the fundamentals of the project: the motivation, objectives and approach in contrast to related work.

Why Robots Should Be Social: Enhancing Machine Learning through Social Human-Robot Interaction

Social learning is a powerful method for cultural propagation of knowledge and skills relying on a complex interplay of learning strategies, social ecology and the human propensity for both learning and tutoring. Social learning has the potential to be an equally potent learning strategy for artificial systems and robots in specific. However, given the complexity and unstructured nature of social learning, implementing social machine learning proves to be a challenging problem. We study one particular aspect of social machine learning: that of offering social cues during the learning interaction. Specifically, we study whether people are sensitive to social cues offered by a learning robot, in a similar way to children’s social bids for tutoring. We use a child-like social robot and a task in which the robot has to learn the meaning of words. For this a simple turn-based interaction is used, based on language games. Two conditions are tested: one in which the robot uses social means to invite a human teacher to provide information based on what the robot requires to fill gaps in its knowledge (i.e. expression of a learning preference); the other in which the robot does not provide social cues to communicate a learning preference. We observe that conveying a learning preference through the use of social cues results in better and faster learning by the robot. People also seem to form a “mental model” of the robot, tailoring the tutoring to the robot’s performance as opposed to using simply random teaching. In addition, the social learning shows a clear gender effect with female participants being responsive to the robot’s bids, while male teachers appear to be less receptive. This work shows how additional social cues in social machine learning can result in people offering better quality learning input to artificial systems, resulting in improved learning performance.

Lighthead robotic face

Summary form only given. This video is about a new kind of robotic head. Through back-projection of a computer generated video into a half-translucent mask, the LightHead robotic head has many advantages compared to tradition mechatronic robotic faces. These advantages are most notably the versatility and ease of controlling facial expressions and creating new faces, the total weight and the low costs. By mounting the head on a robotic arm and equipping it with face detection software, the robot can interact with people in a natural manner.

Tracking gaze over time in HRI as a proxy for engagement and attribution of social agency

In this contribution, we describe a method of analysing and interpreting the direction and timing of a human’s gaze over time towards a robot whilst interacting. Based on annotated video recordings of the interactions, this post-hoc analysis can be used to determine how this gaze behaviour changes over the course of an interaction, following from the observation that humans change their behaviour towards the robot on the time-scale of individual interactions. We posit that given these circumstances, this measure may be used as

Active robot learning with human tutelage

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“And what is a Seasnake?” Modelling the acquisition of concept prototypes in a developmental framework

proxy (among others) for engagement in the interaction or the human’s attribution of social agency to the robot. Application of this method to a sample of unstructured childrobot interactions demonstrates its use, and justifies its utilisation in future studies. Categories and Subject Descriptors H.1.2 [Models and Principles]: User/Machine Systems; D.2.8 [Software Engineering]: Metrics— complexity measures, performance measures General Terms Experimentation, Measurement, Theory