Nonverbal Communication During Human-Robot Object Handover. Improving Predictability of Humanoid Robots by Gaze and Gestures in Close Interaction

Meyer zu Borgsen S. Nonverbal Communication During Human-Robot Object Handover. Improving Predictability of Humanoid Robots by Gaze and Gestures in Close Interaction. Bielefeld: Universität Bielefeld; 2020.This doctoral thesis investigates the influence of nonverbal communication on human-robot object handover. Handing objects to one another is an everyday activity where two individuals cooperatively interact. Such close interactions incorporate a lot of nonverbal communication in order to create alignment in space and time. Understanding and transferring communication cues to robots becomes more and more important as e.g. service robots are expected to closely interact with humans in the near future. Their tasks often include delivering and taking objects. Thus, handover scenarios play an important role in human-robot interaction. A lot of work in this field of research focuses on speed, accuracy, and predictability of the robot’s movement during object handover. Still, robots need to be enabled to closely interact with naive users and not only experts. In this work I present how nonverbal communication can be implemented in robots to facilitate smooth handovers. I conducted a study on people with different levels of experience exchanging objects with a humanoid robot. It became clear that especially users with only little experience in regard to interaction with robots rely heavily on the communication cues they are used to on the basis of former interactions with humans. I added different gestures with the second arm, not directly involved in the transfer, to analyze the influence on synchronization, predictability, and human acceptance. Handing an object has a special movement trajectory itself which has not only the purpose of bringing the object or hand to the position of exchange but also of socially signalizing the intention to exchange an object. Another common type of nonverbal communication is gaze. It allows guessing the focus of attention of an interaction partner and thus helps to predict the next action. In order to evaluate handover interaction performance between human and robot, I applied the developed concepts to the humanoid robot Meka M1. By adding the humanoid robot head named Floka Head to the system, I created the Floka humanoid, to implement gaze strategies that aim to increase predictability and user comfort. This thesis contributes to the field of human-robot object handover by presenting study outcomes and concepts along with an implementation of improved software modules resulting in a fully functional object handing humanoid robot from perception and prediction capabilities to behaviors enhanced and improved by features of nonverbal communication

Making intelligent systems team players: Overview for designers

This report is a guide and companion to the NASA Technical Memorandum 104738, 'Making Intelligent Systems Team Players,' Volumes 1 and 2. The first two volumes of this Technical Memorandum provide comprehensive guidance to designers of intelligent systems for real-time fault management of space systems, with the objective of achieving more effective human interaction. This report provides an analysis of the material discussed in the Technical Memorandum. It clarifies what it means for an intelligent system to be a team player, and how such systems are designed. It identifies significant intelligent system design problems and their impacts on reliability and usability. Where common design practice is not effective in solving these problems, we make recommendations for these situations. In this report, we summarize the main points in the Technical Memorandum and identify where to look for further information

Graph-based Reinforcement Learning meets Mixed Integer Programs: An application to 3D robot assembly discovery

Robot assembly discovery is a challenging problem that lives at the intersection of resource allocation and motion planning. The goal is to combine a predefined set of objects to form something new while considering task execution with the robot-in-the-loop. In this work, we tackle the problem of building arbitrary, predefined target structures entirely from scratch using a set of Tetris-like building blocks and a robotic manipulator. Our novel hierarchical approach aims at efficiently decomposing the overall task into three feasible levels that benefit mutually from each other. On the high level, we run a classical mixed-integer program for global optimization of block-type selection and the blocks' final poses to recreate the desired shape. Its output is then exploited to efficiently guide the exploration of an underlying reinforcement learning (RL) policy. This RL policy draws its generalization properties from a flexible graph-based representation that is learned through Q-learning and can be refined with search. Moreover, it accounts for the necessary conditions of structural stability and robotic feasibility that cannot be effectively reflected in the previous layer. Lastly, a grasp and motion planner transforms the desired assembly commands into robot joint movements. We demonstrate our proposed method's performance on a set of competitive simulated RAD environments, showcase real-world transfer, and report performance and robustness gains compared to an unstructured end-to-end approach. Videos are available at https://sites.google.com/view/rl-meets-milp

Verified synthesis of optimal safety controllers for human-robot collaboration

We present a tool-supported approach for the synthesis, verification and validation of the control software responsible for the safety of the human-robot interaction in manufacturing processes that use collaborative robots. In human-robot collaboration, software-based safety controllers are used to improve operational safety, e.g., by triggering shutdown mechanisms or emergency stops to avoid accidents. Complex robotic tasks and increasingly close human-robot interaction pose new challenges to controller developers and certification authorities. Key among these challenges is the need to assure the correctness of safety controllers under explicit (and preferably weak) assumptions. Our controller synthesis, verification and validation approach is informed by the process, risk analysis, and relevant safety regulations for the target application. Controllers are selected from a design space of feasible controllers according to a set of optimality criteria, are formally verified against correctness criteria, and are translated into executable code and validated in a digital twin. The resulting controller can detect the occurrence of hazards, move the process into a safe state, and, in certain circumstances, return the process to an operational state from which it can resume its original task. We show the effectiveness of our software engineering approach through a case study involving the development of a safety controller for a manufacturing work cell equipped with a collaborative robot

개발 초기 단계에서의 소셜 AI 개인비서 평가 모델 개발

학위논문(박사) -- 서울대학교대학원 : 공과대학 산업공학과, 2022.2. 윤명환.This dissertation aims to propose a user evaluation model to evaluate social AI personal assistants in the early stage of product development. Due to the rapid development of personal devices, data generated from personal devices are increasing explosively, and various personal AI services and products using these data are being launched. However, compared to the interest in AI personal assistant products, its market is still immature. In this case, it is important to understand consumer expectations and perceptions deeply and develop a product that can satisfy them to spread the product and allow general consumers to easily accept the product promptly. Accordingly, this dissertation proposes and validates a user evaluation model that can be used in the early stage of product development. Prior to proposing this methodology, main characteristics of social AI personal assistants, the importance of user evaluation in the early stage of product development and the limitations of the existing user evaluation model were investigated in Chapter 2. Various technology acceptance models and evaluation models for social AI personal assistant products have been proposed, evaluation models that can be applied in the initial stage of product development were insufficient, however. Moreover, it was found that commonly used evaluation measures for assessment of hedonic value were much fewer compared to measures for utilitarian value. These were used as starting points of this dissertation. In Chapter 3, the evaluation measures used in previous studies related to social AI personal assistant were collected and carefully reviewed. Through systematic review of 40 studies, the evaluation measures used in the past and limitation of related research were investigated. As a result, it was found that it was not easy to develop a prototype for evaluation, so it was possible to make the most of the products that have already been commercialized. In addition, all evaluation items used in previous studies were collected and used as the basis for the evaluation model to be proposed later. As a result of the analysis, considering the purpose of the social AI personal assistant, the role as supporting the user emotionally through social interaction with the user is important, but it was found that the evaluation measures related to hedonic value that are commonly used were still insufficient. In Chapter 4, evaluation measures that can be used in the initial stage of product development for social AI personal assistant were selected. Selected evaluation measures were used to evaluate three types of social robots and relationship among evaluation factors were induced through this evaluation. A process was proposed to understand to various opinions related to social robots and to derive evaluation items, and a case study was conducted in which a total of 230 people evaluated three social robots concept images using the evaluation items finally selected through this process. As a result, it is shown that consumers’ attitude toward products was built through the utilitarian dimension and the hedonic dimension. In addition, there is positive relationship between ease of use and utility in the utilitarian dimension, and among aesthetic pleasure, attractiveness of personality, affective value in the hedonic dimension. Moreover, it is confirmed that the evaluation model derived from this study showed superior explanatory power compared to the previously proposed technology acceptance model. In Chapter 5, the model was validated again by applying the evaluation measure and the relationship among evaluation factors derived in Chapter 4 to other products. 100 UX experts with expertise in the field of social AI personal assistants and 100 users who use the voice assistant service often, watched two concept videos of the voice assistant service to help users in the onboarding situation of mobile phones and evaluated these concepts. As a result of the evaluation, there is no significant difference in the evaluation results between the UX expert and the real user group, so the structural equation model analysis was conducted using all the data obtained from the UX expert and the real user group. As a result, results similar to those in Chapter 4 are obtained, and it is expected that the model could be generalized to social AI personal assistant products and applied for future research. This dissertation proposes evaluation measure and relationship among evaluation factors that can be applied when conducting user evaluation in the initial stage of social AI personal assistant development. In addition, case studies using social AI personal assistant products and services were conducted to validate it. With the findings of this study, it is expected that researchers who need to conduct user evaluation to clarify product concepts in the early stages of product development will be able to apply evaluation measures effectively. It is expected that the significance of this dissertation will become clearer if further research is conducted comparing the finished product of social AI personal assistants with the video type stimulus in the early stage of development.본 논문은 최근 빠르게 발전하고 있는 social AI personal assistant의 개발 초기 단계에 활용 가능한 사용자 평가 항목을 개발하고 평가 항목 간의 관계를 검증하는 것을 목표로 한다. 개인 디바이스의 발달로 인해, 각 디바이스에서 생성되는 데이터가 폭발적으로 증가하고 있고, 이를 활용한 개인용 AI 서비스 및 제품이 다양하게 제안되고 있다. 하지만 그 관심에 비해, social AI personal assistant 제품의 실제 시장은 아직 성숙하지 않은 단계이다. 이러한 상황에서 제품을 빠르게 확산시키고 일반 소비자들이 쉽게 제품을 수용할 수 있게 하기 위해서는, 소비자의 기대와 인식을 충분히 이해하고 그를 충족시킬 수 있는 제품을 개발하는 것이 중요하다. 이에 따라 본 연구에서는 제품 개발 초기 단계에 활용할 수 있는 사용자 평가 항목을 제안하고 평가 항목 간 관계를 도출하는 것을 목표로 한다. 먼저 2장에서는 social AI personal assistant의 특징, 제품 개발 초기 단계에서 이루어지는 사용자 평가의 중요성 및 기존 사용자 평가 모델의 한계점을 조사하였다. 기존에 기술 수용 모델 및 AI personal assistant 제품의 평가 모델들이 다양하게 제안되어 왔으나, 제품 개발 초기 단계에 활용할 수 있는 평가 모델은 부족하였고, 제품 전반을 평가할 수 있는 평가 모델의 부재로 대부분의 기존 연구에서는 두 가지 이상의 평가 모델을 결합, 수정하여 사용한 것을 알 수 있었다. 3장에서는 AI personal assistant 관련 기존 연구에서 활용된 평가 항목을 검토하였다. 총 40개의 연구를 리뷰하여, 기존에 활용되고 있는 평가 항목의 종류 및 한계점을 알아보았다. 그 결과, 평가를 위한 프로토타입 개발이 쉽지 않기에 이미 상용화된 제품들을 최대한 활용하는 것을 알 수 있었으며, 제품 전반을 평가한 사례는 부족함을 알 수 있었다. 또한 기존 연구들이 사용한 평가 항목을 모두 수집 및 정리하여 이후 제안할 평가 모델의 기반 자료로 활용하였다. 분석 결과, social AI personal assistant의 목적을 고려해보았을 때, 사용자와의 사회적 인터랙션을 통해 사용자의 감정적인 면을 채워주는 역할이 중요하지만, 공통적으로 활용하고 있는 감정적 가치 관련 평가 항목이 부족한 것으로 나타났다. 4장에서는 social AI personal assistant 제품 개발 초기 단계에서 활용 가능한 평가 항목을 수집 및 제안하고, 평가 항목을 활용하여 social robots을 평가한 뒤 이를 통해 평가 항목 간의 관계를 도출하였다. Social robots 관련 의견을 다양하게 청취하고 평가 항목을 도출하는 프로세스를 제안하였으며, 본 프로세스를 통해 최종 선정된 평가 항목을 이용하여, 총 230명이 세 가지 social robots 컨셉 영상을 평가하는 사례 연구를 진행하였다. 평가 결과, 제품에 대한 소비자 태도는 Utilitarian dimension과 Hedonic dimension을 통해 형성되었고, Utilitarian dimension 내 사용성 및 제품 효용성, Hedonic dimension에 포함되는 심미적 만족도, 성격의 매력도, 감성적 가치 각각은 서로 긍정적인 상관관계를 지님을 알 수 있었다. 또한 기존에 제안된 기술 수용 모델 대비 본 연구에서 도출한 평가 모델이 우수한 설명력을 보임을 확인하였다. 5장에서는 4장에서 도출된 평가 모델을 타 제품에 적용하여 모델을 다시 한번 검증하였다. 해당 분야에 전문성을 지닌 UX 전문가 100명 및 음성 비서 서비스를 실제 사용하는 실사용자 100명이, 휴대폰 온보딩 상황에서 사용자를 도와주는 음성 비서 서비스의 컨셉 영상 두 가지를 보고 컨셉에 대한 평가를 진행하였다. 평가 결과 UX 전문가와 실사용자 그룹 간에는 평가 결과에 유의미한 차이를 보이지 않았기 때문에, UX 전문가와 실사용자 그룹에서 얻은 데이터 전체를 활용하여 구조 방정식 모델 분석을 진행하였다. 그 결과 5장과 유사한 수준의 결과를 얻었고, 추후 해당 모델을 social AI personal assistant 제품에 일반화하여 활용할 수 있을 것으로 판단하였다. 본 논문은 social AI personal assistant 관련 제품 및 서비스의 개발 초기 단계에서 사용자 평가를 진행할 때 활용 가능한 평가 항목 및 평가 항목 간의 관계를 도출하였다. 또한 이를 검증하기 위하여 social AI personal assistant 제품 및 서비스를 활용한 사례연구를 진행하였다. 본 연구 결과는 추후 제품 개발 초기 단계에서 제품의 컨셉을 명확히 하기 위한 사용자 평가를 실시해야 하는 연구진이 효율적으로 활용할 수 있을 것으로 기대된다. 추후 이 부분의 검증을 위해, social AI personal assistants의 완제품과 개발 초기 단계의 video type stimulus를 비교하는 추가 연구가 이루어진다면 본 연구의 의미를 보다 명확하게 제시할 수 있을 것으로 생각된다.Chapter 1 Introduction 1 1.1 Background and motivation 1 1.1 Research objectives 5 1.2 Dissertation outline 7 Chapter 2 Literature review 9 2.1 Social AI personal assistant 9 2.2 User centered design process 13 2.3 Technology acceptance models 16 2.4 Evaluation measures for social AI personal assistant 22 2.5 Existing evaluation methodologies for social AI personal assistant 27 Chapter 3 Collection of existing evaluation measures for social AI personal assistants 40 3.1 Background 40 3.2 Methodology 43 3.3 Result 51 3.4 Discussion 60 Chapter 4 Development of an evaluation model for social AI personal assistants 63 4.1 Background 63 4.2 Methodology 66 4.2.1 Developing evaluation measures for social AI personal assistants 68 4.2.2 Conducting user evaluation for social robots 74 4.3 Result 77 4.3.1 Descriptive statistics 77 4.3.2 Hypothesis development and testing 80 4.3.3 Comparison with existing technology acceptance models 88 4.4 Discussion 93 Chapter 5 Verification of an evaluation model with voice assistant services 95 5.1 Background 95 5.2 Methodology 98 5.2.1 Design of evaluation questionnaires for voice assistant services 99 5.2.2 Validation of relationship among evaluation factors 103 5.3 Result 108 5.3.1 Descriptive statistics 108 5.3.2 Hypothesis development and testing 111 5.3.3 Comparison with existing technology acceptance models 118 5.4 Discussion 121 Chapter 6 Conclusion 124 6.1 Summary of this study 124 6.2 Contribution of this study 126 6.3 Limitation and future work 128 Bibliography 129 Appendix A. Evaluation measures for social AI personal assistant collected in Chapter 4 146 Appendix B. Questionnaires for evaluation of social robots 154 Appendix C. Questionnaires for evaluation of voice assistant service 166박

A Corroborative Approach to Verification and Validation of Human--Robot Teams

We present an approach for the verification and validation (V&V) of robot assistants in the context of human-robot interactions (HRI), to demonstrate their trustworthiness through corroborative evidence of their safety and functional correctness. Key challenges include the complex and unpredictable nature of the real world in which assistant and service robots operate, the limitations on available V&V techniques when used individually, and the consequent lack of confidence in the V&V results. Our approach, called corroborative V&V, addresses these challenges by combining several different V&V techniques; in this paper we use formal verification (model checking), simulation-based testing, and user validation in experiments with a real robot. We demonstrate our corroborative V&V approach through a handover task, the most critical part of a complex cooperative manufacturing scenario, for which we propose some safety and liveness requirements to verify and validate. We construct formal models, simulations and an experimental test rig for the HRI. To capture requirements we use temporal logic properties, assertion checkers and textual descriptions. This combination of approaches allows V&V of the HRI task at different levels of modelling detail and thoroughness of exploration, thus overcoming the individual limitations of each technique. Should the resulting V&V evidence present discrepancies, an iterative process between the different V&V techniques takes place until corroboration between the V&V techniques is gained from refining and improving the assets (i.e., system and requirement models) to represent the HRI task in a more truthful manner. Therefore, corroborative V&V affords a systematic approach to 'meta-V&V,' in which different V&V techniques can be used to corroborate and check one another, increasing the level of certainty in the results of V&V