A Turing-Like Handshake Test for Motor Intelligence

Abstract. In the Turing test, a computer model is deemed to “think intelligently ” if it can generate answers that are not distinguishable from those of a human. This test is limited to the linguistic aspects of machine intelligence. A salient function of the brain is the control of movement, with the human hand movement being a sophisticated demonstration of this function. Therefore, we propose a Turing-like handshake test, for machine motor intelligence. We administer the test through a telerobotic system in which the interrogator is engaged in a task of holding a robotic stylus and interacting with another party (human, artificial, or a linear combination of the two). Instead of asking the interrogator whether the other party is a person or a computer program, we employ a forced-choice method and ask which of two systems is more humanlike. By comparing a given model with a weighted sum of human and artificial systems, we fit a psychometric curve to the answers of the interrogator and extract a quantitative measure for the computer model in terms of similarity to the human handshake

Evaluation of the Handshake Turing Test for anthropomorphic Robots

Handshakes are fundamental and common greeting and parting gestures among humans. They are important in shaping first impressions as people tend to associate character traits with a person's handshake. To widen the social acceptability of robots and make a lasting first impression, a good handshaking ability is an important skill for social robots. Therefore, to test the human-likeness of a robot handshake, we propose an initial Turing-like test, primarily for the hardware interface to future AI agents. We evaluate the test on an android robot's hand to determine if it can pass for a human hand. This is an important aspect of Turing tests for motor intelligence where humans have to interact with a physical device rather than a virtual one. We also propose some modifications to the definition of a Turing test for such scenarios taking into account that a human needs to interact with a physical medium.Comment: Accepted as a Late Breaking Report in The 15th Annual ACM/IEEE International Conference on Human Robot Interaction (HRI) 202

Human-Robot Handshaking: A Review

For some years now, the use of social, anthropomorphic robots in various situations has been on the rise. These are robots developed to interact with humans and are equipped with corresponding extremities. They already support human users in various industries, such as retail, gastronomy, hotels, education and healthcare. During such Human-Robot Interaction (HRI) scenarios, physical touch plays a central role in the various applications of social robots as interactive non-verbal behaviour is a key factor in making the interaction more natural. Shaking hands is a simple, natural interaction used commonly in many social contexts and is seen as a symbol of greeting, farewell and congratulations. In this paper, we take a look at the existing state of Human-Robot Handshaking research, categorise the works based on their focus areas, draw out the major findings of these areas while analysing their pitfalls. We mainly see that some form of synchronisation exists during the different phases of the interaction. In addition to this, we also find that additional factors like gaze, voice facial expressions etc. can affect the perception of a robotic handshake and that internal factors like personality and mood can affect the way in which handshaking behaviours are executed by humans. Based on the findings and insights, we finally discuss possible ways forward for research on such physically interactive behaviours.Comment: Pre-print version. Accepted for publication in the International Journal of Social Robotic

Advances in Human-Robot Handshaking

The use of social, anthropomorphic robots to support humans in various industries has been on the rise. During Human-Robot Interaction (HRI), physically interactive non-verbal behaviour is key for more natural interactions. Handshaking is one such natural interaction used commonly in many social contexts. It is one of the first non-verbal interactions which takes place and should, therefore, be part of the repertoire of a social robot. In this paper, we explore the existing state of Human-Robot Handshaking and discuss possible ways forward for such physically interactive behaviours.Comment: Accepted at The 12th International Conference on Social Robotics (ICSR 2020) 12 Pages, 1 Figur

Turing-Test Evaluation of a Mobile Haptic Virtual Reality Kissing Machine

Various communication systems have been developed to integrate the haptic channel in digital communication. Future directions of such haptic technologies are moving towards realistic virtual reality applications and human-robot social interaction. With the digitisation of touch, robots equipped with touch sensors and actuators can communicate with humans on a more emotional and intimate level, such as sharing a hug or kiss just like humans do. This paper presents the design guideline, implementation and evaluations of a novel haptic kissing machine for smart phones - the Kissenger machine. The key novelties and contributions of the paper are: (i) A novel haptic kissing device for mobile phones, which uses dynamic perpendicular force stimulation to transmit realistic sensations of kissing in order to enhance intimacy and emotional connection of digital communication; (ii) Extensive evaluations of the Kissenger machine, including a lab experiment that compares mediated kissing with Kissenger to real kissing, a unique haptic Turing test that involves the first academic study of humanmachine kiss, and a field study of the effects of Kissenger on long distance relationships

The Multimodal Turing Test for Realistic Humanoid Robots with Embodied Artificial Intelligence

Alan Turing developed the Turing Test as a method to determine whether artificial intelligence (AI) can deceive human interrogators into believing it is sentient by competently answering questions at a confidence rate of 30%+. However, the Turing Test is concerned with natural language processing (NLP) and neglects the significance of appearance, communication and movement. The theoretical proposition at the core of this paper: ‘can machines emulate human beings?’ is concerned with both functionality and materiality. Many scholars consider the creation of a realistic humanoid robot (RHR) that is perceptually indistinguishable from a human as the apex of humanity’s technological capabilities. Nevertheless, no comprehensive development framework exists for engineers to achieve higher modes of human emulation, and no current evaluation method is nuanced enough to detect the causal effects of the Uncanny Valley (UV) effect. The Multimodal Turing Test (MTT) provides such a methodology and offers a foundation for creating higher levels of human likeness in RHRs for enhancing human-robot interaction (HRI

Physical Analysis of Handshaking Between Humans: Mutual Synchronisation and Social Context

International audienceOne very popular form of interpersonal interaction used in various situations is the handshake (HS), which is an act that is both physical and social. This article aims to demonstrate that the paradigm of synchrony that refers to the psychology of individuals' temporal movement coordination could also be considered in handshaking. For this purpose, the physical features of the human HS are investigated in two different social situations: greeting and consolation. The duration and frequency of the HS and the force of the grip have been measured and compared using a prototype of a wearable system equipped with several sensors. The results show that an HS can be decomposed into four phases, and after a short physical contact, a synchrony emerges between the two persons who are shaking hands. A statistical analysis conducted on 31 persons showed that, in the two different contexts, there is a significant difference in the duration of HS, but the frequency of motion and time needed to synchronize were not impacted by the context of an interaction

Stephen Harper as killer robot

In popular culture and public discourse, especially on the Internet, the image of Canada’s former Prime Minister Stephen Harper is conspicuously characterized and caricatured as robotic [...] Amidst popular culture’s hordes of anthropomorphized robots, Harper attained a peculiarly converse characterization as a robotized anthropomorph. [...] The image of Stephen Harper as killer robot figures anxieties about the automation of governance and ensuing loss of democracy. The image of Harper as robot provides a suggestive case for analyzing Canadian popular culture and the spectre of an automated body politic. This essay documents and theorizes the pattern of critical representations of the Harper government of 2006 to 2015 in popular culture, especially in digital media

Building Machines That Learn and Think Like People

Recent progress in artificial intelligence (AI) has renewed interest in building systems that learn and think like people. Many advances have come from using deep neural networks trained end-to-end in tasks such as object recognition, video games, and board games, achieving performance that equals or even beats humans in some respects. Despite their biological inspiration and performance achievements, these systems differ from human intelligence in crucial ways. We review progress in cognitive science suggesting that truly human-like learning and thinking machines will have to reach beyond current engineering trends in both what they learn, and how they learn it. Specifically, we argue that these machines should (a) build causal models of the world that support explanation and understanding, rather than merely solving pattern recognition problems; (b) ground learning in intuitive theories of physics and psychology, to support and enrich the knowledge that is learned; and (c) harness compositionality and learning-to-learn to rapidly acquire and generalize knowledge to new tasks and situations. We suggest concrete challenges and promising routes towards these goals that can combine the strengths of recent neural network advances with more structured cognitive models.Comment: In press at Behavioral and Brain Sciences. Open call for commentary proposals (until Nov. 22, 2016). https://www.cambridge.org/core/journals/behavioral-and-brain-sciences/information/calls-for-commentary/open-calls-for-commentar