A Study of Optimized EEG Signal Induction/Extraction Techniques for Basic Motion Control of Personal Robots for Physically Impaired Users

When interfaces utilizing EEG signals are used, these signals can be induced through diverse association techniques. In the present article, differences in recognition performance of the systems related to robot control techniques according to diverse association techniques have been analyzed. For this purpose, elements that can affect the performance other than association techniques were excluded as much as possible, and the focus was placed on the performance analysis per association technique. According to the study results of the present article, the movement association technique based on gestures showed the highest recognition performance in the robot control system for multiple users. In the robot control system for single users, hearing-based phonation association is considered to show the highest recognition performance. In the study results of the present article, superior recognition performance is considered to be derived than the recognition performance displayed by existing systems when applied to the robot control area utilizing EEG signals.

“Siri, Show Me Scary Images of AI”: Effects of Text-Based Frames and Visuals on Support for Artificial Intelligence

This research note examines how framing influences attitudes toward artificial intelligence (AI). It uses an experiment embedded in a nationally representative online survey to test the effects of text-based frames and visuals on opinion about developing, funding, and banning AI. Participants exposed to a “social progress” frame reported greater support for AI than those exposed to a “Pandora’s box” frame. Images (virtual assistants, personal robots, menacing movie AIs, or none) did not influence opinion by themselves but interacted with textual frames to do so. The results extend our understanding of framing effects on public attitudes toward emerging technologies.

Toward privacy-sensitive human–robot interaction: Privacy terms and human–data interaction in the personal robot era

Can we have personal robots without giving away personal data? Besides, what is the role of a robots Privacy Policy in that question? This work explores for the first time privacy in the context of consumer robotics through the lens of information communicated to users through Privacy Policies and Terms and Conditions. Privacy, personal and non-personal data are discussed under the light of the human–robot relationship, while we attempt to draw connections to dimensions related to personalization, trust, and transparency. We introduce a novel methodology to assess how the “Organization for Economic Cooperation and Development Guidelines Governing the Protection of Privacy and Trans-Border Flows of Personal Data” are reflected upon the publicly available Privacy Policies and Terms and Conditions in the consumer robotics field. We draw comparisons between the ways eight consumer robotic companies approach privacy principles. Current findings demonstrate significant deviations in the structure and context of privacy terms. Some practical dimensions in terms of improving the context and the format of privacy terms are discussed. The ultimate goal of this work is to raise awareness regarding the various privacy strategies used by robot companies while ultimately creating a usable way to make this information more relevant and accessible to users.

Navigation Model for a Robot as a Human Group Member to Adapt to Changing Conditions of Personal Space

In the present paper, we propose a robotic model to help determine a robot’s position under the changing conditions of human personal space in a human-robot group. Recently, several attempts have been made to develop personal robots suitable for human communities. Determining a robot’s position is important not only to avoid collisions with humans but also to maintain a socially acceptable distance from them. Interpersonal space maintained by persons in a community depends on the particular context and situations. Therefore, robots need to determine their own positions while considering the positions of other persons and evaluating the changes made in their personal space. To address this problem, we proposed a robot navigation model and examined whether the experiment participants could distinguish the robot’s trajectory from the human’s trajectory in the experimental scenario. We prepared a scenario in which robots in a group needed to keep an appropriate distance in a three-dimensional space. The experiment participants provided their impressions on robot movements while watching the records representing the scenario. The results indicate that (1) a robot using the proposed model is able to follow the other group members and (2) the experiment participants were not sure whether the trajectories of the robots were controlled by humans and by the proposed model. Therefore, we conclude that the proposed model generates suitable trajectories in robot groups.

The Pransky interview: Dr. Tessa Lau, Founder and CEO of Dusty Robotics

Purpose The purpose of this paper is to provide a “Q&A interview” conducted by Joanne Pransky of Industrial Robot Journal as a method to impart the combined technological, business and personal experience of a prominent, robotic industry PhD-turned successful business leader, regarding the commercialization and challenges of bringing technological inventions to market. The paper aims to discuss these issues. Design/methodology/approach The interviewee is Dr Tessa Lau, an experienced entrepreneur with expertise in AI, machine learning, and robotics, who thrives on the challenges of creating startups. She is currently Founder/CEO at Dusty Robotics, whose mission is to address construction industry productivity by introducing robotic automation on the jobsite. In this interview, Lau discusses her technical and business insights from the startups she built. Findings Dr Lau received her BA and BS from Cornell University in computer science and applied & engineering physics; and an MS and PhD degree in computer science from University of Washington. Prior to co-founding Dusty in April 2018, she was CTO/co-founder at Savioke, where she orchestrated the deployment of 75+ delivery robots into hotels and high-rises. Previously, Lau was a research scientist at Willow Garage, where she developed simple interfaces for personal robots. She also spent 11 years at IBM Research working in business process automation and knowledge capture. Originality/value Dr Lau, known as the Chief Robot Whisperer, is a robot industry disruptor who is passionate about pioneering technology that gives people super-powers. Lau has built two businesses, large, successful venture capital-funded companies. Lau was named 2017 Woman of Influence by The Silicon Valley Business Journal and one of the most creative business people by Fast Company in 2015. Over the years, Lau has served on program committees for various major HCI and AI conferences and on the board for the CRA-W – the committee for the status of women in computing research.