2,686 research outputs found

    Embedding Robotic Agents in the Social Environment

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    This paper discusses the interactive vision approach, which advocates using knowledge from the human sciences on the structure and dynamics of human-human interaction in the development of machine vision systems and interactive robots. While this approach is discussed generally, the particular case of the system being developed for the Aurora project (which aims to produce a robot to be used as a tool in the therapy of children with autism) is especially considered, with description of the design of the machine vision system being employed and discussion of ideas from the human sciences with particular reference to the Aurora system. An example architecture for a simple interactive agent, which will likely form the basis for the first implementation of this system, is briefly described and a description of hardware used for the Aurora system is given.Peer reviewe

    Post-Turing Methodology: Breaking the Wall on the Way to Artificial General Intelligence

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    This article offers comprehensive criticism of the Turing test and develops quality criteria for new artificial general intelligence (AGI) assessment tests. It is shown that the prerequisites A. Turing drew upon when reducing personality and human consciousness to “suitable branches of thought” re-flected the engineering level of his time. In fact, the Turing “imitation game” employed only symbolic communication and ignored the physical world. This paper suggests that by restricting thinking ability to symbolic systems alone Turing unknowingly constructed “the wall” that excludes any possi-bility of transition from a complex observable phenomenon to an abstract image or concept. It is, therefore, sensible to factor in new requirements for AI (artificial intelligence) maturity assessment when approaching the Tu-ring test. Such AI must support all forms of communication with a human being, and it should be able to comprehend abstract images and specify con-cepts as well as participate in social practices

    Human-Robot Trust Integrated Task Allocation and Symbolic Motion planning for Heterogeneous Multi-robot Systems

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    This paper presents a human-robot trust integrated task allocation and motion planning framework for multi-robot systems (MRS) in performing a set of tasks concurrently. A set of task specifications in parallel are conjuncted with MRS to synthesize a task allocation automaton. Each transition of the task allocation automaton is associated with the total trust value of human in corresponding robots. Here, the human-robot trust model is constructed with a dynamic Bayesian network (DBN) by considering individual robot performance, safety coefficient, human cognitive workload and overall evaluation of task allocation. Hence, a task allocation path with maximum encoded human-robot trust can be searched based on the current trust value of each robot in the task allocation automaton. Symbolic motion planning (SMP) is implemented for each robot after they obtain the sequence of actions. The task allocation path can be intermittently updated with this DBN based trust model. The overall strategy is demonstrated by a simulation with 5 robots and 3 parallel subtask automata

    A Review of Verbal and Non-Verbal Human-Robot Interactive Communication

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    In this paper, an overview of human-robot interactive communication is presented, covering verbal as well as non-verbal aspects of human-robot interaction. Following a historical introduction, and motivation towards fluid human-robot communication, ten desiderata are proposed, which provide an organizational axis both of recent as well as of future research on human-robot communication. Then, the ten desiderata are examined in detail, culminating to a unifying discussion, and a forward-looking conclusion

    Anticipation in Human-Robot Cooperation: A Recurrent Neural Network Approach for Multiple Action Sequences Prediction

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    Close human-robot cooperation is a key enabler for new developments in advanced manufacturing and assistive applications. Close cooperation require robots that can predict human actions and intent, and understand human non-verbal cues. Recent approaches based on neural networks have led to encouraging results in the human action prediction problem both in continuous and discrete spaces. Our approach extends the research in this direction. Our contributions are three-fold. First, we validate the use of gaze and body pose cues as a means of predicting human action through a feature selection method. Next, we address two shortcomings of existing literature: predicting multiple and variable-length action sequences. This is achieved by introducing an encoder-decoder recurrent neural network topology in the discrete action prediction problem. In addition, we theoretically demonstrate the importance of predicting multiple action sequences as a means of estimating the stochastic reward in a human robot cooperation scenario. Finally, we show the ability to effectively train the prediction model on a action prediction dataset, involving human motion data, and explore the influence of the model's parameters on its performance. Source code repository: https://github.com/pschydlo/ActionAnticipationComment: IEEE International Conference on Robotics and Automation (ICRA) 2018, Accepte

    로봇의 고개를 움직이는 동작과 타이밍이 인간과 로봇의 상호작용에 미치는 효과

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    학위논문(석사) -- 서울대학교대학원 : 인문대학 협동과정 인지과학전공, 2023. 2. Sowon Hahn.In recent years, robots with artificial intelligence capabilities have become ubiquitous in our daily lives. As intelligent robots are interacting closely with humans, social abilities of robots are increasingly more important. In particular, nonverbal communication can enhance the efficient social interaction between human users and robots, but there are limitations of behavior expression. In this study, we investigated how minimal head movements of the robot influence human-robot interaction. We newly designed a robot which has a simple shaped body and minimal head movement mechanism. We conducted an experiment to examine participants' perception of robots different head movements and timing. Participants were randomly assigned to one of three movement conditions, head nodding (A), head shaking (B) and head tilting (C). Each movement condition included two timing variables, prior head movement of utterance and simultaneous head movement with utterance. For all head movement conditions, participants' perception of anthropomorphism, animacy, likeability and intelligence were higher compared to non-movement (utterance only) condition. In terms of timing, when the robot performed head movement prior to utterance, perceived naturalness was rated higher than simultaneous head movement with utterance. The findings demonstrated that head movements of the robot positively affects user perception of the robot, and head movement prior to utterance can make human-robot conversation more natural. By implementation of head movement and movement timing, simple shaped robots can have better social interaction with humans.최근 인공지능 로봇은 일상에서 흔하게 접할 수 있는 것이 되었다. 인간과의 교류가 늘어남에 따라 로봇의 사회적 능력은 더 중요해지고 있다. 인간과 로봇의 사회적 상호작용은 비언어적 커뮤니케이션을 통해 강화될 수 있다. 그러나 로봇은 비언어적 제스처의 표현에 제약을 갖는다. 또한 로봇의 응답 지연 문제는 인간이 불편한 침묵의 순간을 경험하게 한다. 본 연구를 통해 로봇의 고개 움직임이 인간과 로봇의 상호작용에 어떤 영향을 미치는지 알아보았다. 로봇의 고개 움직임을 탐구하기 위해 단순한 형상과 고개를 움직이는 구조를 가진 로봇을 새롭게 디자인하였다. 이 로봇을 활용하여 로봇의 머리 움직임과 타이밍이 참여자에게 어떻게 지각되는지 실험하였다. 참여자들은 3가지 움직임 조건인, 끄덕임 (A), 좌우로 저음 (B), 기울임 (C) 중 한 가지 조건에 무작위로 선정되었다. 각각의 고개 움직임 조건은 두 가지 타이밍(음성보다 앞선 고개 움직임, 음성과 동시에 일어나는 고개 움직임)의 변수를 갖는다. 모든 타입의 고개 움직임에서 움직임이 없는 조건과 비교하여 로봇의 인격화, 활동성, 호감도, 감지된 지능이 향상된 것을 관찰하였다. 타이밍은 로봇의 음성보다 고개 움직임이 앞설 때 자연스러움이 높게 지각되는 것으로 관찰되었다. 결과적으로, 로봇의 고개 움직임은 사용자의 지각에 긍정적인 영향을 주며, 앞선 타이밍의 고개 움직임이 자연스러움을 향상시키는 것을 확인하였다. 고개를 움직이는 동작과 타이밍을 통해 단순한 형상의 로봇과 인간의 상호작용이 향상될 수 있음을 본 연구를 통해 확인하였다.Chapter 1. Introduction 1 1.1. Motivation 1 1.2. Literature Review and Hypotheses 3 1.3. Purpose of Study 11 Chapter 2. Experiment 13 2.1. Methods 13 2.2. Results 22 2.3. Discussion 33 Chapter 3. Conclusion 35 Chapter 4. General Discussion 37 4.1. Theoretical Implications 37 4.2. Practical Implications 38 4.3. Limitations and Future work 39 References 41 Appendix 53 Abstract in Korean 55석
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