Should beat gestures be learned or designed? A benchmarking user study

In this paper, we present a user study on gener-ated beat gestures for humanoid agents. It has been shownthat Human-Robot Interaction can be improved by includingcommunicative non-verbal behavior, such as arm gestures. Beatgestures are one of the four types of arm gestures, and are knownto be used for emphasizing parts of speech. In our user study,we compare beat gestures learned from training data with hand-crafted beat gestures. The first kind of gestures are generatedby a machine learning model trained on speech audio andhuman upper body poses. We compared this approach with threehand-coded beat gestures methods: designed beat gestures, timedbeat gestures, and noisy gestures. Forty-one subjects participatedin our user study, and a ranking was derived from pairedcomparisons using the Bradley Terry Luce model. We found thatfor beat gestures, the gestures from the machine learning modelare preferred, followed by algorithmically generated gestures.This emphasizes the promise of machine learning for generating communicative actions.QC 20190815</p

Hierarchical reinforcement learning as creative problem solving

publisher: Elsevier articletitle: Hierarchical reinforcement learning as creative problem solving journaltitle: Robotics and Autonomous Systems articlelink: http://dx.doi.org/10.1016/j.robot.2016.08.021 content_type: article copyright: © 2016 Elsevier B.V. All rights reserved

Security Risks of Social Robots Used to Persuade and Manipulate

Earlier research has shown that robots can provoke social responses in people, and that robots often elicit compliance. In this paper we discuss three proof of concept studies in which we explore the possibility of robots being hacked and taken over by others with the explicit purpose of using the robot's social capabilities. Three scenarios are explored: gaining access to secured areas, extracting sensitive and personal information, and convincing people to take unsafe action. We find that people are willing to do these tasks, and that social robots tend to be trusted, even in situations that would normally cause suspicion

Qualitative review of object recognition techniques for tabletop manipulation

This paper provides a qualitative review of different object recognition techniques relevant for near-proximity Human- Robot Interaction. These techniques are divided into three categories: 2D correspondence, 3D correspondence and nonvision based methods. For each technique an implementation is chosen that is representative of the existing technology to provide a broad review to assist in selecting an appropriate method for tabletop object recognition manipulation. For each of these techniques we give their strengths and weaknesses based on defined criteria. We then discuss and provide recommendations for each of them

The Free-play Sandbox: a Methodology for the Evaluation of Social Robotics and a Dataset of Social Interactions

conference paperEvaluating human-robot social interactions in a rigorous manner is notoriously difficult: studies are either conducted in labs with constrained protocols to allow for robust measurements and a degree of replicability, but at the cost of ecological validity; or in the wild, which leads to superior experimental realism, but often with limited replicability and at the expense of rigorous interaction metrics. We introduce a novel interaction paradigm, designed to elicit rich and varied social interactions while having desirable scientific properties (replicability, clear metrics, possibility of either autonomous or Wizard-of-Oz robot behaviours). This paradigm focuses on child-robot interactions, and builds on a sandboxed free-play environment. We present the rationale and design of the interaction paradigm, its methodological and technical aspects (including the open-source implementation of the software platform), as well as two large open datasets acquired with this paradigm, and meant to act as experimental baselines for future research

Leveraging Human Inputs in Interactive Machine Learning for Human Robot Interaction

A key challenge of HRI is allowing robots to be adaptable, especially as robots are expected to penetrate society at large and to interact in unexpected environments with non- technical users. One way of providing this adaptability is to use Interactive Machine Learning, i.e. having a human supervisor included in the learning process who can steer the action selection and the learning in the desired direction. We ran a study exploring how people use numeric rewards to evaluate a robot's behaviour and guide its learning. From the results we derive a number of challenges when design- ing learning robots: what kind of input should the human provide? How should the robot communicate its state or its intention? And how can the teaching process by made easier for human supervisors

Have I got the power? Analysing and reporting statistical power in HRI

This article presents a discussion of the importance of power analyses, providing an overview of when power analyses should be run in the context of the field of Human-Robot Interaction, as well as some examples of how to perform a power analysis. This work was motivated by the observation that the majority of papers published in the proceedings of recent HRI conferences did not report conducting a power analysis; an observation that has concerning implications for many conclusions drawn by these studies. This work is intended to raise awareness and encourage researchers to conduct power analyses when designing research studies using human participants