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12 results on '"School of of Electrical and Computer Engineering [Athens] (School of E.C.E)"'

1. Task Driven Skill Learning in a Soft-Robotic Arm*

Author

Paris Oikonomou, Athanasios Dometios, Mehdi Khamassi, Costas S. Tzafestas, Institut des Systèmes Intelligents et de Robotique (ISIR), Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU), School of of Electrical and Computer Engineering [Athens] (School of E.C.E), National Technical University of Athens [Athens] (NTUA), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Khamassi, Mehdi, SoftGrip: Functionalised Soft robotic gripper for delicate produce harvesting powered by imitation learning-based control - H2020-ICT-2018-20 SOFTGRIP - 101017054 - INCOMING, Architectures et modèles d'Adptation et de la cognition (AMAC), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), This research has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 101017054 (project: SoftGrip). This research was also partially supported by the French Centre National de la Recherche Scientifique (CNRS) PICS international scheme no. 279521., IEEE, and European Project: 101017054,H2020-ICT-2018-20 SOFTGRIP

Subjects
0209 industrial biotechnology, [SPI] Engineering Sciences [physics], [INFO.INFO-RB] Computer Science [cs]/Robotics [cs.RO], Robot Learning, 02 engineering and technology, [INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG], Soft Robotics, [INFO] Computer Science [cs], [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], [SPI]Engineering Sciences [physics], 020901 industrial engineering & automation, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], Reinforcement learning, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], [INFO]Computer Science [cs], Probabilistic Movement Primitives
Abstract

International audience; In this paper we introduce a novel technique that aims to dynamically control a two-module bio-inspired softrobotic arm in order to qualitatively reproduce a path defined by sparse way-points. The main idea behind this work is based on the assumption that a complex trajectory may be derived as a combination of a discrete set of parameterizable simple movements, as suggested by Movement Primitive (MP) theory. Capitalising on recent advanced in this field, the proposed controller uses a Probabilistic MP (ProMP) model which initially creates an abstract mapping in the primitive-level between the task and the actuation space, and subsequently guides the movement's composition by exploiting its unique properties-conditioning and blending. At the same time, a learning-based adaptive controller updates the composition parameters by estimating the inverse kinematics of the robot, while an auxiliary process through replanning ensures that the trajectory complies with the new estimation. The learning architecture is evaluated on both a simulation model, and a real soft-robotic arm. The research findings show that the proposed methodology constitutes a novel approach that successfully manages to simplify the trajectory control task for robots of complex dynamics when high-precision is not required.

Published
2021

2. MRI Vocal Tract Sagittal Slices Estimation during Speech Production of CV

Author

Chrysanthi Dourou, Jacques Felblinger, Ajinkya Kulkarni, Pierre-André Vuissoz, Yu Xie, Yves Laprie, Karyna Isaieva, Ioannis Douros, Laprie, Yves, Speech Modeling for Facilitating Oral-Based Communication (MULTISPEECH), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Natural Language Processing & Knowledge Discovery (LORIA - NLPKD), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Department of Neurology, Wuhan University [China], School of of Electrical and Computer Engineering [Athens] (School of E.C.E), National Technical University of Athens [Athens] (NTUA), Centre d'investigation clinique [Nancy] (CIC), Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lorraine (UL), Imagerie Adaptative Diagnostique et Interventionnelle (IADI), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lorraine (UL), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), and Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)

Subjects
Speech production, Signal processing, speech resources enrichment, Computer science, [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, Speech recognition, 020206 networking & telecommunications, 02 engineering and technology, Sagittal plane, [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL], Set (abstract data type), RtMRI data, medicine.anatomical_structure, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, [INFO.INFO-CL] Computer Science [cs]/Computation and Language [cs.CL], vocal tract, 0202 electrical engineering, electronic engineering, information engineering, medicine, Frame (artificial intelligence), 020201 artificial intelligence & image processing, Image transformation, Vocal tract
Abstract

International audience; In this paper we propose an algorithm for estimating vocal tract para sagittal slices in order to have a better overview of the behaviour of the articulators during speech production. The first step is to align the consonant-vowel (CV) data of the sagittal plains between them for the train speaker. Sets of transformations that connect the midsagittal frames with the neighbouring ones is acquired for the train speaker. Another set of transformations is calculated which transforms the midsagittal frames of the train speaker to the corresponding midsagittal frames of the test speaker and is used to adapt to the test speaker domain the previously computed sets of transformations. The newly adapted transformations are applied to the midsagittal frames of the test speaker in order to estimate the neighbouring sagittal frames. Several mono speaker models are combined to produce the final frame estimation. To evaluate the results, image cross-correlation between the original and the estimated frames was used. Results show good agreement between the original and the estimated frames.

Published
2021
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3. Synthesize MRI vocal tract data during CV production

Author

Ioannis Douros, Chrysanthi Dourou, Yu Xie, jacques Felblinger, Karyna Isaieva, Pierre-André Vuissoz, Yves Laprie, Speech Modeling for Facilitating Oral-Based Communication (MULTISPEECH), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Natural Language Processing & Knowledge Discovery (LORIA - NLPKD), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Imagerie Adaptative Diagnostique et Interventionnelle (IADI), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lorraine (UL), School of of Electrical and Computer Engineering [Athens] (School of E.C.E), National Technical University of Athens [Athens] (NTUA), Department of Neurology, Wuhan University [China], Centre d'investigation clinique [Nancy] (CIC), Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lorraine (UL), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL), Université de Lorraine (UL)-Institut National de la Santé et de la Recherche Médicale (INSERM), and Laprie, Yves

Subjects
[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, [INFO.INFO-CL] Computer Science [cs]/Computation and Language [cs.CL], [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL]
Abstract

International audience; A set of rtMR image transformations across time is computed during the production of CV that is afterwards applied to a new speaker in order to synthesize his/her CV pseudo rtMRI data. Synthesized images are compared with the original ones using image cross-correlation. 2 Purpose To be able to enlarge MRI speech corpus by synthesizing data.

Published
2020

4. Vocal tract sagittal slices estimation from MRI midsagittal slices during speech production of CV

Author

Ioannis Douros, Yu Xie, Chrysanthi Dourou, jacques Felblinger, Karyna Isaieva, Pierre-André Vuissoz, Yves Laprie, Speech Modeling for Facilitating Oral-Based Communication (MULTISPEECH), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Natural Language Processing & Knowledge Discovery (LORIA - NLPKD), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Imagerie Adaptative Diagnostique et Interventionnelle (IADI), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lorraine (UL), Department of Neurology, Wuhan University [China], School of of Electrical and Computer Engineering [Athens] (School of E.C.E), National Technical University of Athens [Athens] (NTUA), Centre d'investigation clinique [Nancy] (CIC), Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lorraine (UL), Laprie, Yves, Université de Lorraine (UL)-Institut National de la Santé et de la Recherche Médicale (INSERM), and Université de Lorraine (UL)-Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy)-Institut National de la Santé et de la Recherche Médicale (INSERM)

Subjects
[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, [INFO.INFO-CL] Computer Science [cs]/Computation and Language [cs.CL], [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, ComputingMilieux_MISCELLANEOUS, [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL]
Abstract

International audience

Published
2020

5. Adaptive reinforcement learning with active state-specific exploration for engagement maximization during simulated child-robot interaction

Author

Theodore Tsitsimis, George Velentzas, Iñaki Rañó, Costas S. Tzafestas, Mehdi Khamassi, School of of Electrical and Computer Engineering [Athens] (School of E.C.E), National Technical University of Athens [Athens] (NTUA), Faculty of Computing and Engineering [University of Ulster], University of Ulster, Institut des Systèmes Intelligents et de Robotique (ISIR), and Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)

Subjects
reinforcement learning, Technology, Meta learning (computer science), Computer science, Cognitive Neuroscience, joint action, 02 engineering and technology, Robot learning, Human–robot interaction, Task (project management), 03 medical and health sciences, Behavioral Neuroscience, human-robot interaction, 0302 clinical medicine, meta-learning, Developmental Neuroscience, Artificial Intelligence, Human–computer interaction, 0202 electrical engineering, electronic engineering, information engineering, Reinforcement learning, State space, 030206 dentistry, Maximization, Human-Computer Interaction, Robot, autonomous robotics, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], 020201 artificial intelligence & image processing, active exploration, engagement
Abstract

Using assistive robots for educational applications requires robots to be able to adapt their behavior specifically for each child with whom they interact.Among relevant signals, non-verbal cues such as the child’s gaze can provide the robot with important information about the child’s current engagement in the task, and whether the robot should continue its current behavior or not. Here we propose a reinforcement learning algorithm extended with active state-specific exploration and show its applicability to child engagement maximization as well as more classical tasks such as maze navigation. We first demonstrate its adaptive nature on a continuous maze problem as an enhancement of the classic grid world. There, parameterized actions enable the agent to learn single moves until the end of a corridor, similarly to “options” but without explicit hierarchical representations.We then apply the algorithm to a series of simulated scenarios, such as an extended Tower of Hanoi where the robot should find the appropriate speed of movement for the interacting child, and to a pointing task where the robot should find the child-specific appropriate level of expressivity of action. We show that the algorithm enables to cope with both global and local non-stationarities in the state space while preserving a stable behavior in other stationary portions of the state space. Altogether, these results suggest a promising way to enable robot learning based on non-verbal cues and the high degree of non-stationarities that can occur during interaction with children.

Published
2018
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6. Stochastic stability in Max-Product and Max-Plus Systems with Markovian Jumps

Author

George P. Papavassilopoulos, Petros Maragos, Ioannis Kordonis, School of of Electrical and Computer Engineering [Athens] (School of E.C.E), National Technical University of Athens [Athens] (NTUA), and Dept. of Electrical and Computer Engineering

Subjects
Lyapunov function, 0209 industrial biotechnology, Stochastic stability, Markov process, Systems and Control (eess.SY), 02 engineering and technology, 01 natural sciences, 010104 statistics & probability, symbols.namesake, 020901 industrial engineering & automation, Stability theory, FOS: Electrical engineering, electronic engineering, information engineering, FOS: Mathematics, Nonlinear systems, [INFO.INFO-SY]Computer Science [cs]/Systems and Control [cs.SY], Applied mathematics, 0101 mathematics, Electrical and Electronic Engineering, Mathematics - Optimization and Control, Mathematics, Max-plus systems, Stochastic systems, State (functional analysis), Optimization and Control (math.OC), Control and Systems Engineering, Product (mathematics), symbols, Computer Science - Systems and Control, [MATH.MATH-OC]Mathematics [math]/Optimization and Control [math.OC], Isomorphism, Focus (optics)
Abstract

International audience; We study Max-Product and Max-Plus Systems with Markovian Jumps and focus on stochastic stability problems. At first, a Lyapunov function is derived for the asymptotically stable deterministic Max-Product Systems. This Lyapunov function is then adjusted to derive sufficient conditions for the stochastic stability of Max-Product systems with Markovian Jumps. Many step Lyapunov functions are then used to derive necessary and sufficient conditions for stochastic stability. The results for the Max-Product systems are then applied to Max-Plus systems with Markovian Jumps, using an isomorphism and almost sure bounds for the asymptotic behavior of the state are obtained. A numerical example illustrating the application of the stability results on a production system is also given.

Published
2018
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7. Using Silence MR Image to Synthesise Dynamic MRI Vocal Tract Data of CV

Author

Chrysanthi Dourou, Pierre-André Vuissoz, Karyna Isaieva, Yu Xie, Jacques Felblinger, Ioannis Douros, Ajinkya Kulkarni, Yves Laprie, Laprie, Yves, Speech Modeling for Facilitating Oral-Based Communication (MULTISPEECH), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Natural Language Processing & Knowledge Discovery (LORIA - NLPKD), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Imagerie Adaptative Diagnostique et Interventionnelle (IADI), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lorraine (UL), School of of Electrical and Computer Engineering [Athens] (School of E.C.E), National Technical University of Athens [Athens] (NTUA), Department of Neurology, Wuhan University [China], Centre d'investigation clinique [Nancy] (CIC), Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lorraine (UL), Université de Lorraine (UL)-Institut National de la Santé et de la Recherche Médicale (INSERM), and Université de Lorraine (UL)-Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy)-Institut National de la Santé et de la Recherche Médicale (INSERM)

Subjects
Computer science, [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, Speech recognition, Frame (networking), 020206 networking & telecommunications, 02 engineering and technology, [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL], Image (mathematics), Set (abstract data type), Silence, 030507 speech-language pathology & audiology, 03 medical and health sciences, image transformation, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, [INFO.INFO-CL] Computer Science [cs]/Computation and Language [cs.CL], vocal tract, Dynamic contrast-enhanced MRI, 0202 electrical engineering, electronic engineering, information engineering, Mr images, 0305 other medical science, Speech resources enrichment, Vocal tract, rtMRI data, pseudo rtMRI synthesis
Abstract

International audience; In this work we present an algorithm for synthesising pseudo rtMRI data of the vocal tract. rtMRI data on the midsagittal plane were used to synthesise target consonant-vowel (CV) using only a silence frame of the target speaker. For this purpose, several single speaker models were created. The input of the algorithm is a silence frame of both train and target speaker and the rtMRI data of the target CV. An image transformation is computed from each CV frame to the next one, creating a set of transformations that describe the dynamics of the CV production. Another image transformation is computed from the silence frame of train speaker to the silence frame of the target speaker and is used to adapt the set of transformations computed previously to the target speaker. The adapted set of transformations is applied to the silence of the target speaker tosynthesise his/her CV pseudo rtMRI data. Synthesised images from multiple single speaker models are frame aligned and then averaged to create the final version of synthesised images. Synthesised images are compared with the original ones using image cross-correlation. Results show good agreement between the synthesised and the original images.

Published
2020

8. A Novel Reinforcement-Based Paradigm for Children to Teach the Humanoid Kaspar Robot

Author

Luke Jai Wood, Costas S. Tzafestas, Farshid Amirabdollahian, Ben Robins, Kerstin Dautenhahn, Mehdi Khamassi, Gabriella Lakatos, Abolfazl Zaraki, University of Hertfordshire [Hatfield] (UH), Institut cellule souche et cerveau / Stem Cell and Brain Research Institute (U1208 Inserm - UCBL1 / SBRI - USC 1361 INRAE), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), School of of Electrical and Computer Engineering [Athens] (School of E.C.E), National Technical University of Athens [Athens] (NTUA), University of Waterloo [Waterloo], Institut des Systèmes Intelligents et de Robotique (ISIR), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (MITI ROBAUTISTE and PICS 279521), European Project: 687831,H2020,H2020-ICT-2015,BabyRobot(2016), Gestionnaire, HAL Sorbonne Université 5, and Child-Robot Communication and Collaboration: Edutainment, Behavioural Modelling and Cognitive Development in Typically Developing and Autistic Spectrum Children - BabyRobot - - H20202016-01-01 - 2018-12-31 - 687831 - VALID

Subjects
General Computer Science, Social Psychology, Process (engineering), media_common.quotation_subject, Autism, Control (management), 050105 experimental psychology, Human–robot interaction, 03 medical and health sciences, 0302 clinical medicine, Reinforcement learning, medicine, Autonomous robotics, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], 0501 psychology and cognitive sciences, Electrical and Electronic Engineering, Reinforcement learning algorithm, Reinforcement, media_common, Enthusiasm, business.industry, Teaching, [INFO.INFO-RB] Computer Science [cs]/Robotics [cs.RO], 05 social sciences, Robotics, medicine.disease, Human-Computer Interaction, Philosophy, Control and Systems Engineering, Robot, Children social skills, Artificial intelligence, business, Psychology, 030217 neurology & neurosurgery, Cognitive psychology
Abstract

This paper presents a contribution aiming at testing novel child–robot teaching schemes that could be used in future studies to support the development of social and collaborative skills of children with autism spectrum disorders (ASD). We present a novel experiment where the classical roles are reversed: in this scenario the children are the teachers providing positive or negative reinforcement to the Kaspar robot in order for it to learn arbitrary associations between different toy names and the locations where they are positioned. The objective is to stimulate interaction and collaboration between children while teaching the robot, and also provide them tangible examples to understand that sometimes learning requires several repetitions. To facilitate this game, we developed a reinforcement learning algorithm enabling Kaspar to verbally convey its level of uncertainty during the learning process, so as to better inform the children about the reasons behind its successes and failures. Overall, 30 typically developing (TD) children aged between 7 and 8 (19 girls, 11 boys) and 9 children with ASD performed 25 sessions (16 for TD; 9 for ASD) of the experiment in groups, and managed to teach Kaspar all associations in 2 to 7 trials. During the course of study Kaspar only made rare unexpected associations (2 perseverative errors and 2 win-shifts, within a total of 314 trials), primarily due to exploratory choices, and eventually reached minimal uncertainty. Thus, the robot’s behaviour was clear and consistent for the children, who all expressed enthusiasm in the experiment.

Published
2019
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9. Linear Search by a Pair of Distinct-Speed Robots

Author

Tomasz Kociumaka, Jurek Czyzowicz, David Ilcinkas, Leszek Gąsieniec, Ralf Klasing, Dominik Pająk, Evangelos Bampas, School of of Electrical and Computer Engineering [Athens] (School of E.C.E), National Technical University of Athens [Athens] (NTUA), Département d'Informatique et d'Ingénierie (DII), Université du Québec en Outaouais (UQO), Department of Computer Science [Liverpool], University of Liverpool, Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB), University of Warsaw (UW), Institute of informatics [Wrocław], See paper for details., ANR-11-BS02-0014,DISPLEXITY,Calculabilité et complexité en distribué(2011), ANR-13-JS02-0002,MACARON,Bouger et Calculer: Agents, Robots et Réseaux(2013), ANR-16-CE40-0023,DESCARTES,Abstraction modulaire pour le calcul distribué(2016), ANR-10-IDEX-0003,IDEX BORDEAUX,Initiative d'excellence de l'Université de Bordeaux(2010), Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory, Pajak, Dominik S, Laboratoire d'informatique Fondamentale de Marseille (LIF), Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS), Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS), Institute of Informatics [Warsaw], Faculty of Mathematics, Informatics, and Mechanics [Warsaw] (MIMUW), University of Warsaw (UW)-University of Warsaw (UW), and Centre National de la Recherche Scientifique (CNRS)-École Centrale de Marseille (ECM)-Aix Marseille Université (AMU)

Subjects
Engineering, General Computer Science, Computer science, different speeds, [INFO.INFO-DS]Computer Science [cs]/Data Structures and Algorithms [cs.DS], 0211 other engineering and technologies, 0102 computer and information sciences, 02 engineering and technology, Bang-bang robot, linear search, 01 natural sciences, Article, Computer Science::Robotics, Position (vector), mobile robots, 0202 electrical engineering, electronic engineering, information engineering, Cartesian coordinate robot, Point (geometry), Computer vision, [INFO]Computer Science [cs], Online algorithm, Simulation, Linear search, 021103 operations research, business.industry, Applied Mathematics, Mobile robot, Computer Science Applications, 010201 computation theory & mathematics, group search, Moment (physics), Line (geometry), Robot, 020201 artificial intelligence & image processing, Artificial intelligence, [INFO.INFO-DC]Computer Science [cs]/Distributed, Parallel, and Cluster Computing [cs.DC], business
Abstract

Two mobile robots are initially placed at the same point on an infinite line. Each robot may move on the line in either direction not exceeding its maximal speed. The robots need to find a stationary target placed at an unknown location on the line. The search is completed when both robots arrive at the target point. The target is discovered at the moment when either robot arrives at its position. The robot knowing the placement of the target may communicate it to the other robot. We look for the algorithm with the shortest possible search time (i.e. the worst-case time at which both robots meet at the target) measured as a function of the target distance from the origin (i.e. the time required to travel directly from the starting point to the target at unit velocity). We consider two standard models of communication between the robots, namely wireless communication and communication by meeting. In the case of communication by meeting, a robot learns about the target while sharing the same location with a robot possessing this knowledge. We propose here an optimal search strategy for two robots including the respective lower bound argument, for the full spectrum of their maximal speeds. This extends the main result of Chrobak et al. (in: Italiano, Margaria-Steffen, Pokorný, Quisquater, Wattenhofer (eds) Current trends in theory and practice of computer science, SOFSEM, 2015) referring to the exact complexity of the problem for the case when the speed of the slower robot is at least one third of the faster one. In the wireless communication model, a message sent by one robot is instantly received by the other robot, regardless of their current positions on the line. For this model, we design a strategy which is optimal whenever the faster robot is at most \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sqrt{17}+4\approx 8.123$$\end{document}17+4≈8.123 times faster than the slower one. We also prove that otherwise the wireless communication offers no advantage over communication by meeting.

Published
2019
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10. Robot Fast Adaptation to Changes in Human Engagement During Simulated Dynamic Social Interaction With Active Exploration in Parameterized Reinforcement Learning

Author

Khamassi, Mehdi, Velentzas, George, Tsitsimis, Theodore, Tzafestas, Costas, Institut des Systèmes Intelligents et de Robotique (ISIR), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), School of of Electrical and Computer Engineering [Athens] (School of E.C.E), and National Technical University of Athens [Athens] (NTUA)

Subjects
[SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]
Abstract

International audience; Dynamic uncontrolled human-robot interactions (HRIs) require robots to be able to adapt to changes in the human's behavior and intentions. Among relevant signals, non-verbal cues such as the human's gaze can provide the robot with important information about the human's current engagement in the task, and whether the robot should continue its current behavior or not. However, robot reinforcement learning (RL) abilities to adapt to these nonverbal cues are still underdeveloped. Here, we propose an active exploration algorithm for RL during HRI where the reward function is the weighted sum of the human's current engagement and variations of this engagement. We use a parameterized action space where a meta-learning algorithm is applied to simultaneously tune the exploration in discrete action space (e.g., moving an object) and in the space of continuous characteristics of movement (e.g., velocity, direction, strength, and expressivity). We first show that this algorithm reaches state-of-the-art performance in the nonstationary multiarmed bandit paradigm. We then apply it to a simulated HRI task, and show that it outper-forms continuous parameterized RL with either passive or active exploration based on different existing methods. We finally test the performance in a more realistic test of the same HRI task, where a practical approach is followed to estimate human engagement through visual cues of the head pose. The algorithm can detect and adapt to perturbations in human engagement with different durations. Altogether, these results suggest a novel efficient and robust framework for robot learning during dynamic HRI scenarios.

Published
2018
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11. A Framework for Robot Learning During Child-Robot Interaction with Human Engagement as Reward Signal

Author

Costas S. Tzafestas, George Velentzas, Mehdi Khamassi, Theodore Tsitsimis, Georgia Chalvatzaki, Institut des Systèmes Intelligents et de Robotique (ISIR), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), School of of Electrical and Computer Engineering [Athens] (School of E.C.E), and National Technical University of Athens [Athens] (NTUA)

Subjects
Engagement, Joint attention, Computer science, 05 social sciences, Active Explo- ration, Autonomous Robotics, Gaze, Robot learning, Reinforcement Learning, 050105 experimental psychology, Task (project management), 03 medical and health sciences, 0302 clinical medicine, HRI, Action (philosophy), Human–computer interaction, Joint Action, Task analysis, Reinforcement learning, Robot, 0501 psychology and cognitive sciences, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], 030217 neurology & neurosurgery
Abstract

International audience; Using robots as therapeutic or educational tools for children with autism requires robots to be able to adapt their behavior specifically for each child with whom they interact. In particular, some children may like to be looked into the eyes by the robot while some may not. Some may like a robot with an extroverted behavior while others may prefer a more introverted behavior. Here we present an algorithm to adapt the robot's expressivity parameters of action (mutual gaze duration, hand movement expressivity) in an online manner during the interaction. The reward signal used for learning is based on an estimation of the child's mutual engagement with the robot, measured through non-verbal cues such as the child's gaze and distance from the robot. We first present a pilot joint attention task where children with autism interact with a robot whose level of expressivity is predetermined to progressively increase, and show results suggesting the need for online adaptation of expressivity. We then present the proposed learning algorithm and some promising simulations in the same task. Altogether, these results suggest a way to enable robot learning based on non-verbal cues and to cope with the high degree of non-stationarities that can occur during interaction with children.

Published
2018
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12. Intangible Cultural Heritage and New Technologies: Challenges and Opportunities for Cultural Preservation and Development

Author

Sotiris Manitsaris, Joëlle Tillmane, Michela Ott, Claire Pillot-Loiseau, Marilena Alivizatou-Barakou, Alexandros Kitsikidis, Francesca Pozzi, L. Buchman, Kosmas Dimitropoulos, Martine Adda-Decker, Filareti Tsalakanidou, Vasileios Charisis, Yilmaz Erdal, Leontios J. Hadjileontiadis, Samuel Dupont, Stelios Hadjidimitriou, Spiros Nikolopoulos, Nikos Grammalidis, Athanasios Manitsaris, Christina Volioti, Bruce Denby, Samer Al Kork, Benjamin Picart, Chantas Giannis, Marius Cotescu, University College of London [London] (UCL), School of of Electrical and Computer Engineering [Athens] (School of E.C.E), National Technical University of Athens [Athens] (NTUA), Centre for Research and Technology Hellas [Athènes] (CERTH), Centre for Research and Technology Hellas (CERTH), Laboratoire Signaux, Modèles et Apprentissage Statistique (SIGMA), Ecole Superieure de Physique et de Chimie Industrielles de la Ville de Paris (ESPCI Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS), LPP - Laboratoire de Phonétique et Phonologie - UMR 7018 (LPP), Université Sorbonne Nouvelle - Paris 3-Centre National de la Recherche Scientifique (CNRS), University of Mons [Belgium] (UMONS), Interactions Sol Plante Atmosphère (UMR ISPA), Institut National de la Recherche Agronomique (INRA)-Ecole Nationale Supérieure des Sciences Agronomiques de Bordeaux-Aquitaine (Bordeaux Sciences Agro), Institute for Educational Technology = The Istituto per le Tecnologie Didattiche (ITD), Turk Telekom, Informatics Systems and Applications Group [Aristotle University of Thessaloniki], Aristotle University of Thessaloniki, Université Aristote de Thessalonique, University of Macedonia [Thessaloniki] (UoM), Centre de Robotique (CAOR), Mines Paris - PSL (École nationale supérieure des mines de Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL), M. Ioannides et al. (eds.), European Project: 600676,EC:FP7:ICT,FP7-ICT-2011-9,I-TREASURES(2013), Centre National de la Recherche Scientifique (CNRS)-Ecole Superieure de Physique et de Chimie Industrielles de la Ville de Paris (ESPCI Paris), MINES ParisTech - École nationale supérieure des mines de Paris, Centre for Research and Technology Hellas [Athène] (CERTH), Centre for Research & Technology Hellas (CERTH), Laboratoire Signaux, Modèles et Apprentissage Statistique, ESPCI ParisTech-Centre National de la Recherche Scientifique (CNRS), Interactions Sol Plante Atmosphère (ISPA), Istituto Tecnologie Didattiche, Consiglio Nazionale delle Ricerche (CNR), University of Macedonia [Thessaloniki], and MINES ParisTech - École nationale supérieure des mines de Paris-PSL Research University (PSL)

Subjects
Intangible cultural heritage, Emerging technologies, 02 engineering and technology, Safeguarding, Political science, 11. Sustainability, 0202 electrical engineering, electronic engineering, information engineering, Cultural heritage management, Industrial heritage, [SHS.LANGUE]Humanities and Social Sciences/Linguistics, Scope (project management), business.industry, Environmental resource management, transmission, OS, 020207 software engineering, safeguarding, Cultural heritage, 3D Visualization, game-like educational applications, semantic analysis, Intangible Cultural Heritage, Information and Communications Technology, ICT, 020201 artificial intelligence & image processing, Engineering ethics, business
Abstract

International audience; Intangible Cultural Heritage (ICH) is a relatively recent term coined to represent living cultural expressions and practices, which are recognised by communities as distinct aspects of identity. The safeguarding of ICH has become a topic of international concern primarily through the work of UNESCO (United Nations Educational, Scientific and Cultural Organisation). However, little research has been done on the role of new technologies in the preservation and transmission of intangible heritage. The chapter examines resources, projects and technologies providing access to ICH and identifies gaps and constraints. It draws on research conducted within the scope of the collaborative research project , i-Treasures. In so doing, it covers the state of the art in technologies that could be employed for access, capture and analysis of ICH in order to highlight how specific new technologies can contribute to the transmission and safeguarding of ICH.

Published
2017
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