Sera Buyukgoz

Institut Systèmes Intelligents et de Robotique

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Mohamed Chétouani

Institut Systèmes Intelligents et de Robotique

Karen Tatarian

Sorbonne Université

Sebastian Wallkötter

Uppsala University

Jasmin Grosinger

Graz University of Technology

Marine Chamoux

SoftBank Robotics (France)

Rebecca Stower

KTH Royal Institute of Technology

Alessandro Saffiotti

Örebro University

Amit Kumar Pandey

Bihar Agricultural University

Aaquib Tabrez

University of Colorado Boulder

Steven Lawrence

University of Waterloo

Cooperative Institutions

Middle East Technical University

Centre National de la Recherche Scientifique

SoftBank Robotics (France)

Institute of Electrical and Electronics Engineers

University of Waterloo

Massachusetts Institute of Technology

Institut Systèmes Intelligents et de Robotique

Sorbonne Université

Sorbonne Paris Cité

University of Plymouth

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Two ways to make your robot proactive: Reasoning about human intentions or reasoning about possible futures

Frontiers in Robotics and AI (2022)

Sera Buyukgoz Jasmin Grosinger Mohamed Chétouani Alessandro Saffiotti

Robots sharing their space with humans need to be proactive to be helpful. Proactive robots can act on their own initiatives in an anticipatory way to benefit humans. In this work, we investigate two ways to make robots proactive. One way is to recognize human intentions and to act to fulfill them, like opening the door that you are about to cross. The other way is to reason about possible future threats or opportunities and to act to prevent or to foster them, like recommending you to take an umbrella since rain has been forecast. In this article, we present approaches to realize these two types of proactive behavior. We then present an integrated system that can generate proactive robot behavior by reasoning on both factors: intentions and predictions. We illustrate our system on a sample use case including a domestic robot and a human. We first run this use case with the two separate proactive systems, intention-based and prediction-based, and then run it with our integrated system. The results show that the integrated system is able to consider a broader variety of aspects that are required for proactivity.

10.3389/frobt.2022.929267

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Apprentice: advances toward the development of a cobot helper in assembly lines

Institution of Engineering and Technology eBooks (2023)

Yunus Terzioğlu Özgür Aslan Burak Bolat Sera Buyukgoz Zeynep Sonat Baltacı

Collaborative robotic manipulators, also known as cobots, are designed to work safely alongside humans, with the objective of taking industrial automation to the next level by increasing the efficiency of a human worker. The worldwide market size for cobots is projected to grow fast, and cobots are expected to accelerate the automation of frequently changing tasks, mainly in medium and small businesses. This brings in many challenges pertaining to (i) how easily such robots can be used and how much programming and design they require, (ii) how user friendly and helpful they are, and (iii) how appealing they are as coworkers.In this chapter, we present our vision of deploying cobots on the assembly lines toward improving the productivity of the workers. Our vision is based on the observation that human workers are superior to cobots in manipulation tasks and that carrying out the versatile manipulation tasks of an assembly-line worker at the same speed and finesse is likely to remain beyond the reach of cobots in the near future.Specifically, we report the advances we have made within ÇIRAK and its successor KALFA (Apprentice and Journeymen in Turkish) projects, referred to as an umbrella "Apprentice Project" in the rest of the chapter.

Apprenticeship

Trouble shooting

10.1049/pbce134e_ch10

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2nd Edition of Solutions for Socially Intelligent HRI in Real-World Scenarios (SSIR-HRI)

Karen Tatarian Sera Buyukgoz Marine Chamoux Aaquib Tabrez Bradley Hayes

Today it seems even more evident that social robots will have a more integral role to play in the real-world scenarios and need to participate in the full richness of human society. Central to the success of robots being socially intelligent agents is insuring effective interactions between humans and robots. In order to achieve that goal, researchers and engineers from both industry and academia need to come together to share ideas, trials, failures, and successes. This workshops aims at creating the bridge between industry and academia and as such creating a community to tackle the current and future challenges of socially intelligent human-robot interaction in real-world scenarios by finding solutions for them.

Bridge (graph theory)

Human–robot interaction

Social robot

10.1145/3434074.3444874

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Towards Socially Intelligent Robots In Real World Applications: Challenges and Intricacies

HAL (Le Centre pour la Communication Scientifique Directe) (2022)

Mélanie Jouaiti Sera Buyukgoz Eloïse Zehnder Steven Lawrence Amir Aly

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Exploring Behavioral Creativity of a Proactive Robot

Frontiers in Robotics and AI (2021)

Sera Buyukgoz Amit Kumar Pandey Marine Chamoux Mohamed Chétouani

Creativity, in one sense, can be seen as an effort or action to bring novelty. Following this, we explore how a robot can be creative by bringing novelty in a human-robot interaction (HRI) scenario. Studies suggest that proactivity is closely linked with creativity. Proactivity can be defined as acting or interacting by anticipating future needs or actions. This study aims to explore the effect of proactive behavior and the relation of such behaviors to the two aspects of creativity: 1) the perceived creativity observed by the user in the robot's proactive behavior and 2) creativity of the user by assessing how creativity in HRI can be shaped or influenced by proactivity. We do so by conducting an experimental study, where the robot tries to support the user on the completion of the task regardless of the end result being novel or not and does so by exhibiting anticipatory proactive behaviors. In our study, the robot instantiates a set of verbal communications as proactive robot behavior. To our knowledge, the study is among the first to establish and investigate the relationship between creativity and proactivity in the HRI context, based on user studies. The initial results have indicated a relationship between observed proactivity, creativity, and task achievement. It also provides valuable pointers for further investigation in this domain.

10.3389/frobt.2021.694177

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Citations (5)

MobiAxis: An Embodied Learning Task for Teaching Multiplication with a Social Robot

HAL (Le Centre pour la Communication Scientifique Directe) (2021)

Karen Tatarian Sebastian Wallkötter Sera Buyukgoz Rebecca Stower Mohamed Chétouani

The use of robots in educational settings is growing increasingly popular. Yet, many of the learning tasks involving social robots do not take full advantage of their physical embodiment. MobiAxis is a proposed learning task which uses the physical capabilities of a Pepper robot to teach the concepts of positive and negative multiplication along a number line. The robot is embodied with a number of multi-modal socially intelligent features and behaviours which are designed to enhance learning. This paper is a position paper describing the technical and theoretical implementation of the task, as well as proposed directions for future studies.

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Generating Proactive Communicative Robot Behaviors

HAL (Le Centre pour la Communication Scientifique Directe) (2022)

Sera Buyukgoz

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MobiAxis: An Embodied Learning Task for Teaching Multiplication with a Social Robot

arXiv (Cornell University) (2020)

Karen Tatarian Sebastian Wallkötter Sera Buyukgoz Rebecca Stower Mohamed Chétouani

Social robot

10.48550/arxiv.2004.07806

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Two ways to make your robot proactive: reasoning about human intentions, or reasoning about possible futures

arXiv (Cornell University) (2022)

Sera Buyukgoz Jasmin Grosinger Mohamed Chétouani Alessandro Saffiotti

Robots sharing their space with humans need to be proactive in order to be helpful. Proactive robots are able to act on their own initiative in an anticipatory way to benefit humans. In this work, we investigate two ways to make robots proactive. One way is to recognize humans' intentions and to act to fulfill them, like opening the door that you are about to cross. The other way is to reason about possible future threats or opportunities and to act to prevent or to foster them, like recommending you to take an umbrella since rain has been forecasted. In this paper, we present approaches to realize these two types of proactive behavior. We then present an integrated system that can generate proactive robot behavior by reasoning on both factors: intentions and predictions. We illustrate our system on a sample use case including a domestic robot and a human. We first run this use case with the two separate proactive systems, intention-based and prediction-based, and then run it with our integrated system. The results show that the integrated system is able to take into account a broader variety of aspects that are needed for proactivity.

10.48550/arxiv.2205.05492

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Citations (1)