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Scientific Progress Report Labex SMART

December 2014

Laboratory of Excellence SMART (ANR-11-LABX-65) is supported by French State funds managed by the ANR within the Investissements

d'Avenir Programme under reference ANR-11-IDEX-0004-02

http://www.upmc.fr/http://www.cnrs.fr/http://www.inserm.fr/http://www.mines-telecom.fr/http://www.ircam.fr/http://www.univ-paris8.fr/http://www.ephe.sorbonne.fr/

2

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Table of contents SMART Overview ......................................................................................................................... 5

SMART Projects .......................................................................................................................... 11

EDHHI................................................................................................................................................. 14

ISMES ................................................................................................................................................. 19

ONBUL ............................................................................................................................................... 27

SeNSE ................................................................................................................................................. 35

SMART-BAN ....................................................................................................................................... 49

SpinalCOM ......................................................................................................................................... 59

SMART Actions ........................................................................................................................... 65

SMART Perspectives ................................................................................................................... 85

SMART Publications ................................................................................................................... 89

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5

SMART Overview

6

7

SMART Context In 2010 the French Government launched a program called Investments for the Future with the main

objective of supporting higher education, research and transfer to industry

http://www.enseignementsup-recherche.gouv.fr/pid24578/investissements-d-avenir.html (in French)

Within this program, there were specifically competitive calls in 2010 and 2011 for different

categories of projects among which projects called Equipment of Excellence (Equipex), Laboratories

of Excellence (Labex), and Initiatives of Excellence (Idex). The latter aimed at grouping French

universities within larger structures called Idex. The Labex are local or national consortia of labs that

address a large, long term (8 to 10 years), scientific program. The Equipex program finances new

scientific equipment.

SMART, was submitted to the second call in 2011 and selected by the international committee in

February 2012. The main topic of the project is research on human-machine interactions. The

consortium is composed of eight laboratories affiliated to seven legal institutions: University Pierre

and Marie Curie, CNRS, INSERM, Institut Mines-Telecom, University Paris 8, IRCAM, EPHE.

SMART is within the Idex Sorbonne Universits (SU).

SMART Vision Be it in our homes or workplaces, in the streets of our cities where we stride or the public spaces

where we go for business, service, shopping, leisure, or travel, we are already surrounded with digital

systems, more or less complex computing and communicating devices and artifacts, with which we

interact. Access to this digital world opens enormous possibilities for new services and easier living.

SMART aims to design technologies that would make the interaction of humans with those devices

simpler, more efficient and more adaptive. This requires to include in those systems capacities to

better understand how humans act and interact, and hence to develop models of humans representing

their physical capabilities, psychological trends and behaviors. It also requires studying efficient and

natural interfaces and enhanced tools for a better interactivity between digital artifacts and humans.

The wide distribution in our environment of communicating and interacting devices, integrating

individually and collectively perception, computation, actuation and communication capacities at

different scales, and which may be mobile, creates an ambient intelligence also requires addressing

them both as integrated units and as a global networked system and conceiving infrastructures for their

connectivity. Those devices produce also massive amounts of data that need to be processed efficiently

to extract the relevant information and knowledge, and also stored and protected.

SMART Ambition The SMART Labex objective is to contribute to the foundational bases for facilitating the inclusion of

intelligent digital artifacts in our daily life for service and assistance. The project addresses underlying

scientific questions raised by the development of Human-centered digital systems and artifacts in a

comprehensive way.

An efficient and natural interaction with artifacts requires understanding human actions and behavior,

as well as the design of natural and friendly interfaces. When those artifacts and digital devices are

disseminated, networked, and sometimes mobile, it is also necessary to provide for their connectivity

and for knowledge extraction, sometimes from massive data, knowledge sharing and access.

Project actions unfold along five dimensions: a) basic research and novel concepts; b) methods,

technologies and tools for the design, operation, interfacing and networking of systems and artifacts

interacting with humans; c) exploration of novel applications and usage; d) education curricula, and e)

dissemination and exploitation of results.

As an illustrative main usage area, source of open topics and case studies for this project, the new

services induced by the digital society for e-health, including the ageing society and autonomous

living.

http://www.enseignementsup-recherche.gouv.fr/pid24578/investissements-d-avenir.html

8

The research program is organized along five axes that address the main scientific questions and the

use case:

1. Modeling of humans: understanding and modeling of physiological and neurophysiological functions, integrated representations of the musculoskeletal system, of the basic motor and

perceptive systems and of the learning and adaptation mechanisms, integrative architectures.

2. Interfaces and Interaction with humans: design of new devices that enhance the range of interactions between human and machines (e.g., haptic devices), new interaction modalities

and signals including cognitive and emotional aspects.

3. Humans at the convergence of digital and real environments: large scale complex data processing, knowledge emergence, digital and human decision-making and socially intelligent

computing.

4. Autonomic Distributed Environments for Mobility: networking, virtualization, self-organized, self-healing and secure architectures of heterogeneous, autonomous and

cooperative mobile entities.

5. Human autonomy and e-health: innovative medical devices, from assisting robots to implanted sensors and bio-mechatronic embedded systems, and personalized care in the

context of e-health for autonomous living

SMART Teams SMART is a specific blend of research teams of eight laboratories in applied mathematics, computer

science, robotics, neuroscience, medical imagery, networks and distributed systems, Human-Machine

interaction, electrical engineering in the same campus, with a clear and consistent research program

and an education and training program, experimenting new usages in living labs, and having close ties

with industry.

- The Institute of Intelligent Systems and Robotics - ISIR (UPMC, CNRS): Autonomous and interactive Robotics systems; Mobility; cognitive systems; Robotics and Neuroscience; assistance

to surgical and functional rehabilitation; micromanipulation; Manipulation; Haptics.

- Paris 6 Computer Science Lab - LIP6 (UPMC, CNRS): Decision-making, Intelligent Systems and Operations Research, Databases and Machine-Learning, Networks and Distributed Systems,

Systems-on-chip.

- Human and Artificial Cognition Laboratory - Chart-Lutin (UPMC, University Paris 8, EPHE): pragmatic and semantic interactions of human and artificial systems.

- Electronics and Electromagnetism Laboratory - L2E (UPMC): micro and nano-electronics, communication, physiological parameters monitoring.

- Information processing and Communication Electronics Laboratory LTCI (Institut MinesTelecom, CNRS): Signal processing and image, pattern recognition, 3D object modeling,

conversational agents, multimedia (speech, audio, music, images, video), document analysis,

multimodal biometrics.

- Laboratory Jacques-Louis Lions - LJLL (UPMC, CNRS): Mathematical modeling of physical phenomena in physics, mechanics, biology, medicine, chemistry, information processing,

Economics, finance;

- Laboratory of Biomedical Imagery - LIB (UPMC, CNRS, INSERM): Medical imaging, modeling, image and signal processing, magnetic resonance imaging, microscopy, optical imaging, nuclear

medicine imaging, ultrasound, Alzheimer's, cardiovascular disease, medical oncology,

neurosciences.

- Laboratory of the Technology of Music and Sound STMS (UPMC, CNRS, IRCAM): Instrumental Acoustics, Acoustic and Cognitive Spaces and Sound, Perception and Design

Analysis and sound synthesis, Analysis of musical practices, Real-Time Musical Interactions.

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SMART Strategy

Financing transversal projects to investigate the main scientific topics of the project in a multidisciplinary approach

Financing post-doctoral grants, to attract young scientists with high potential to preform advanced research on novel topics and to explore new venues.

Doctoral program for financing PhD grants for excellent post-graduate students to perform novel cross-disciplinary research on the projects topics in participating laboratories.

Financing internships for master students.

Financing invited visiting professors to bring very high-level senior professionals to partner laboratories for participating and advising in research projects and educating local students.

Involvement in educational curricula in relation with the research program for dissemination of results to the younger generation.

Industrial partnership for exploitation of results.

International partnership and cooperation.

SMART Figures

Duration: 94 months (March 2012-Dec. 2019). Total budget: 5 M.

Personnel directly involved in SMART: 88 faculty and researchers, 4 visiting professors, 19 PhDs, 4 Post-docs, 20 Master Interns.

6 projects were launched in September 2013 for durations ranging between 12 and 48 months.

SMART Actions (2012-2014) 3 calls for internships 2 calls for PhDs 1 call for Post-docs 1 call for visiting professors 1 call for projects Invited colloquia: Rodney Brooks, Claude Berrou Regular seminars Organization of workshops

SMART Governance SMART is coordinated by a Director (Raja Chatila), a Deputy Director (Mohamed Chetouani) and a

Project Manager (Zoitsa Siaplaoura). Three main bodies are involved in managing and overseeing the

Labex: the Executive Committee (EXCOM), the Steering Committee (STEERCOM), and the

Scientific Advisory Board (SAB).

The EXCOM is the operational body of the project. It is composed of the director and deputy director,

a representative of each of the five programs and the person in charge of the Education curricula.

Members:

Habib Benali (LIB), Mohamed Chetouani (ISIR), Patrick Gallinari (LIP6), Patrick Garda (LIP6),

Benot Girard (ISIR), Christophe Marsala (LIP6), Catherine Pelachaud (LTCI), Franck Petit (LIP6),

Agns Roby-Brami (ISIR), Pierre Sens (LIP6), Jean-Luc Zarader

The STEERCOM oversees the general operation of the Labex. It is chaired by a representative of the

main partner institution, the Idex Sorbonne Universits, and composed of:

One representative from each of the other seven legal institutions partners of the Labex

The directors of the eight member laboratories or their representatives

A representative of the Doctoral Training Institute

The SMART Labex director

10

Institutions:

Sorbonne Universits: Vronique Atger

CNRS: Wilfrid Perruquetti

EPHE: Franois Jouen

Institut Mines Tlcom: Yves Grenier

INSERM: Franck Lethimonnier

IRCAM: Hugues Vinet

UPMC: Paul Indelicato

Universit Paris 8: Mario Barra

Doctoral Training Institute: Jean-Dominique Polack

Laboratories:

ISIR: Agns Roby-Brami

L2E: Aziz Benlarbi-Dela

Laboratoire CHART-LUTIN: Charles Tijus

LIB: Pascal Laugier

LIP6: Jean-Claude Bajard

LJLL: Benot Perthame

LTCI: Olivier Capp

STMS: Grard Assayag

The SAB is composed of members external to the Labex partners, belonging to French and foreign

higher education and research institutions and industry in the main scientific domains of SMART:

Etienne Burdet, Imperial College, London, UK

Justine Cassell, Carnegie Mellon University, Pittsburgh, USA

Peter Ford Dominey, INSERM, Lyon, FRANCE

Rachid Guerraoui, EPFL, Lausanne, CH

Philippe Roy, CAP DIGITAL, Paris, FRANCE

Stuart Russell, University of California, Berkeley, USA

Alessandro Vinciarelli, Glasgow University, UK

11

SMART Projects

12

13

PROGRESS REPORT

EDHHI

Date : 07/03/2014

Partners ISIR

Serena Ivaldi (postdoc)

Mohamed Chetouani (professeur) Salvatore Anzalone (postdoc)

Anna-Lise Jouen (postdoc)

Charles Ballarini (stagiaire) *

CHART-LUTIN

Ilaria Gaudiello (doctorante) Sebastien Lefort (stagiaire) *

Elisabetta Zibetti (maitre de conference)

Joelle Provasi (maitre de conference)

Stages financs par EDHHI

Sebastien Lefort, n le 13/12/1981

o Responsable: Elisabetta Zibetti, CHART-LUTIN

o Ecole: Ecole Pratique des Hautes Etudes (EPHE) - 4-14 rue Ferrus 75014 Paris

Charles Ballarini , n le 01/04/1988

o responsable: Mohamed Chetouani, UPMC

o Ecole : EPITA

Liste de publications

International Journals

Ivaldi, S.; Anzalone, S.M.; Rousseau, W.; Sigaud, O.; Chetouani, M. (2014) Robot initiative in a team learning task

increases the rhythm of interaction but not the perceived engagement.

Frontiers in Neurorobotics. Vol 8, No 5, DOI 10.3389/fnbot.2014.00005.

Short papers in International W orkshops

Ivaldi, S.; Anzalone, S.; Rousseau, W.; Sigaud, O.; Chetouani, M. (2013). Cues for making a humanoid child more

human-like during social learning tasks. Workshop on Towards social humanoid robots: what makes

interaction human-like? - IROS 2013.

Rousseau, W.; Anzalone, S.; Chetouani, M.; Sigaud, O.; Ivaldi, S. (2013). Learning object names through shared

attention. Workshop on Developmental Social Robotics - IROS 2013.

Ivaldi, S, ; Anzalone, S.; Rousseau, W.; Sigaud, O.; Chetouani, M. (2014). Robot initiative increases the rhythm

of interaction in a team learning task. Workshop Timing in Human-Robot interaction, in HRI 2014, Bielefeld,

Germany.

14

EDHHI: Engagement During Human-Humanoid Interaction Responsible of the project:

Elisabetta ZIBETTI & Jolle PROVASI [CHART- LUTIN 4004]

Mohamed CHETOUANI [ISIR UMR7222]

Partners:

Institut des Systmes Intelligents et de Robotique (ISIR) UPMC/ CNRS UMR 7222: M. Chetouani (Prof.), S. Ivaldi (Post-Doc), S. Anzalone (Post-Doc)

Laboratoire Cognitions Humaine et Artificielle et plate-forme LUTIN (CHART-LUTIN) UP8/EPHE:

I. Gaudiello (Post-Doc), J. Provasi (Mcf), E. Zibetti (Mcf)

Web site: http://www.smart-labex.fr/index.php?perma=EDHHI

1 The Project at a glance

Context and Objectives

EDHHI, a one-year project, advances the current understanding about the factors influencing

effective human-humanoid physical interaction in cooperative tasks.

First we aim at identifying the factors influencing the human engagement towards the robot

and the task for determining the metrics destined to automatically assess the engagement and the

acceptability of the human subjects. Engagement (Sidner et al, 2005) in collaborative interactions is

characterized by a dynamic and continuous exchange of verbal and non-verbal signals - gestural and

postural carrying out information and communication content (Delaherche et al., 2012). Dynamics

can be modulated by inter-individual or social factors such as degree of extraversion or the a-priori

attitude toward robots. Therefore, we investigate them and take them into account as potential bias of

the engagement metrics during human-humanoid interaction.

Our second aim is to identify the factors influencing the robot functional and social

acceptability, which could be used to enhance interactions behaviors. By functional acceptability we

mean the willingness to use technology for the tasks for which the robot has been created (Salvini et

al., 2010) and by social acceptability we mean the facility to share statements, space and routines with

a non-human agent (Pesty & Duhaut, 2011).

To investigate these questions, in EDHHI, we design an experimental protocol involving

physical and verbal interaction between human participants and the iCub robot during several

cooperative tasks such as handling, assembly, and decision-making. We focus on the interplay

between cognitive and personality differences and behavioral features (speech, motion, gaze, posture)

that can have an impact on the effectiveness of the interaction. Methodologically, we combine the

processing of conventional signals (utterance, posture, contacts) and explicit measures such as

responses to personality tests and post-experimental questionnaires.

References

Delaherche, E., Chetouani, M., Mahdhaoui, A. and Saint-Georges, C. and Viaux, S. and Cohen, D. (2012).

Interpersonal Synchrony : A Survey Of Evaluation Methods Across Disciplines. IEEE Transactions on

Affective Computing. Vol 3 No 3 Pages 349 - 365.

Pesty, S., Duhaut, D. (2011). Acceptability In Interaction: From robots to Embodied Conversational Agents.

Computer graphics theory and applications.

Salvini, P., Laschi, C., & Dario, P. (2010). Design for acceptability: improving robots coexistence in human

http://www.smart-labex.fr/index.php?perma=EDHHI

15

society. International Journal of Social Robotics , 2 (4), 451-460.

Sidner, C. L., Lee, C., Kidd, C. D., Lesh, N., & Rich, C. (2005). Explorations in engagement for humans and

robots. Artificial Intelligence, 166(1), 140-164.

2 Scientific progress and results

Work carried out and outcomes achieved over the period

Between September and December 2013:

We selected few dependent variables to evaluate: i) human engagement towards the collaborative task: e.g. distances; gaze and speech duration... ii) human acceptability towards the robot: e.g.

response (compensative or reciprocal)

We finalized the experimental design of the tasks and we set up the experimental protocol and material

We submitted experimental protocol to the ethical committee of CERES for Approbation of the EDHHI protocol by the Ethical Committee Conseil en Ethique pour les Recherches en Sant

(CERES), Universit Paris Descartes.

We developed software tools for implementing different experimental conditions of the

cooperative interaction between humans and iCub in order for the latter to be able to execute the

selected task in an effective and collaborative way.

Between January and April 2014:

We performed pre-experimental tests on dyadic interaction between ten humans adult while executing a simple cooperative task in order to tune the interaction task scenario.

We received the approbation of the EDHHI protocol by the Ethical Committee

We started an intense experimental phase after recruiting 60 adults participants

Between April and August 2014:

We coded, analyzed (video, audio, interview, questionnaire) and draw conclusion on the influence of selected experimental variations on the engagement of the human towards both the

robot and the task.

Defense of the Master (M2) dissertation Evaluation de lengagement lors dInteractions Homme-

Robot: Effets de lextraversion et de lattitude vis--vis des robots sur lmission de signaux

sociaux " by Sebastien Lefort.

During this period we devoted part of our time in disseminating the EDDHI projects and presenting its

first advances (invited seminars) and in preparing publications.

Figure 2 Interaction between a human participant and the humanoid iCub for a cooperative task

Figure 1 the robot, the experimenter and a voluntary participant during the experiment

16

Results

We assessed the influence of extroversion and negative attitude toward robots on speech and gaze

during a cooperative task, where a human must physically manipulate a robot to assemble an object.

The experiments were carried out with the humanoid iCub and N=56 adult participants.

We found that extrovert people tend to talk more and longer to the robot, whereas they do not look at

the robot more than introverts. On the contrary, we found that people with negative attitude towards

robots tend to look less at the robot than people with a positive attitude.

Correlation between the participants extraversion score (computed by the NEO-PI-R) and their gaze, utterance

frequency and duration during the assembly task

Our results suggest that the engagement models classically used in human-robot interaction should

take into account personality traits.

Correlation between the participants negative attitude towards robots score (computed by the NARS) and their

gaze, utterance frequency and duration during the assembly task

3 Recruitment

From the 1st October to the 30th of May 2014 four internships students have joint the EDDHI team on

the following topics.

[1] Evaluation de lengagement lors dInteractions Homme-Robot: Effets de lextraversion et de

lattitude vis--vis des robots sur lmission de signaux sociaux

[2] Interfaces for experiments of human-humanoid interaction

[3] Analysis of behaviors in human-robot interaction experiments

[4] Acceptability in HRI: implicit and explicit methods

Only two of them (1 and 2) have been hired on the EDHHI budget.

Topic Student name Year Dates Supervisor Laboratory Funded by

Evaluation de lengagement

lors dInteractions Homme-

Robot

Sebastien Lefort M2- EPHE From October 2014

(8 months)

Elisabetta

Zibetti CHART EDDHI

Interfaces for experiments of

human-humanoid

interaction

Charles Ballarini M2- EPITA From October 2014

(8 months) Serena Ivaldi ISIR EDDHI

Analysis of behaviors in

human-robot interaction

experiments

Anais Jeannel de

Thiersant L3 -Psycho Pratt

From March 2014

(2 months)

Jolle Provasi

and Serena

Ivaldi

CHART-

ISIR CHART

Acceptability in HRI:

implicit and explicit

methods

Ilaria Gaudiello 3rd PhD Thesis From Oct 2013

(6 months)

Elisabetta

Zibetti CHART CHART

F igur e 2: D em onst r at ion of t he assembly t ask : 1) t he par t icipant asks t he r obot t o gr asp t he t wo cy l inder s; 2)

t he par t icipant gr abs t he r obot ar m s and dem onst r at es how t o m ove t hem t o al ign t he t wo cy l inder s; 3) t hepar t icipant fi xes t he cy l inder s w i t h som e t ape whi le t he r obot is holding t hem ; 4) t he par t icipant r et r ieves

t he assembled ob ject fr om t he r obot .

Variable Ex t r over sion scor e

Gaze frequency (num./ s) r 2= -0,13 ; p= N.S.

Gaze durat ion (s) r 2= 0,098 ; p= N.S.

U t t er ance fr equency (num./ s) r 2= 0,318 ; p< 0,05

U t t er ance dur at ion (s) r 2= 0,321 ; p< 0,05

Table 1: Cor r elat ion bet ween t he par t icipant s ex-t r over sion scor e (com put ed by N EO-P I -R [4]) and

t hei r gaze and ut t er ance fr equency and dur at iondur ing t he assembly t ask .

frequency and durat ion of ut terances (see Table 1). This

can also be seen in the scat ter graphs in Figure 3.To summarize, the more an individual is ext rovert , the

more he/ she will talk to the robot during an assembly taskto provide inst ruct ions. On the cont rary, as ext roversion

does not have influence on the gaze frequency or durat ion,int roverts will not look at the robot s face less than ext ro-

verts.Therefore, with reference to the research hypothesis ex-

pressed in Sect ion 2, we accept Hypothesis 1, and rejectHypothesis 2.

4.2 Effect of negativeattitude towards robotsThepart icipants averageNARSscorewas45,55 (= 12,74),

which is a neut ral value for the at t itude towards robots. Ta-ble 2 reports the Pearsons correlat ion between the NARS

score of the part icipants and their gaze and ut terance fre-quency and durat ion. The results indicate that the nega-

t ive at t itude does not influence the verbal signal, as there

Fu

ncti

on

al

acce

pta

bil

ity

0

0,225

0,45

0,675

0,9

Robot ics expert ise

0 0,25 0,5 0,75 1

So

cia

l a

cce

pta

bil

ity

0

0,25

0,5

0,75

1

Robot ics expert ise

0 0,25 0,5 0,75 1

0

0,225

0,45

0,675

0,9

N egat ive at t itude towards robot ics

score

0 20 40 60 80

So

cia

l a

cce

pta

bil

ity

0

0,25

0,5

0,75

1

N egat ive at t itude towards robot ics

score

0 20 40 60 80

Fre

que

ncy o

f u

tte

rances

0,05

0,167

0,283

0,4

Extroversion score

55 77 99 121 143 165

y = 0,0009x + 0,1336

Du

ratio

n o

f utt

era

nces

0,05

0,175

0,3

0,425

0,55

Extroversion score

55 77 99 121 143 165

y = 0,0014x + 0,1183

F igur e 3: Scat t er gr aphs show ing t he fr equency(number / seconds) and dur at ion (seconds) of ut t er -

ances of t he par t icipant s (N = 56) , in funct ion of t hei rex t r over sion scor e.

F igur e 2: D em onst r at ion of t he assembly t ask : 1) t he par t icipant asks t he r obot t o gr asp t he t wo cy l inder s; 2)

t he par t icipant gr abs t he r obot ar m s and dem onst r at es how t o m ove t hem t o al ign t he t wo cy l inder s; 3) t hepar t icipant fi xes t he cy l inder s w i t h som e t ape whi le t he r obot is holding t hem ; 4) t he par t icipant r et r ieves

t he assembled ob ject fr om t he r obot .

Variable Ex t r over sion scor e

Gaze frequency (num./ s) r 2= -0,13 ; p= N.S.

Gaze durat ion (s) r 2= 0,098 ; p= N.S.

U t t er ance fr equency (num./ s) r 2= 0,318 ; p< 0,05

U t t er ance dur at ion (s) r 2= 0,321 ; p< 0,05

Table 1: Cor r elat ion bet ween t he par t icipant s ex-t r over sion scor e (com put ed by N EO-P I -R [4]) and

t hei r gaze and ut t er ance fr equency and dur at iondur ing t he assembly t ask .

frequency and durat ion of ut terances (see Table 1). This

can also be seen in the scat ter graphs in Figure 3.To summarize, the more an individual is ext rovert , the

more he/ she will talk to the robot during an assembly taskto provide inst ruct ions. On the cont rary, as ext roversion

does not have influence on the gaze frequency or durat ion,int roverts will not look at the robot s face less than ext ro-

verts.Therefore, with reference to the research hypothesis ex-

pressed in Sect ion 2, we accept Hypothesis 1, and rejectHypothesis 2.

4.2 Effect of negativeattitude towards robotsThepart icipants averageNARSscorewas45,55 (= 12,74),

which is a neut ral value for the at t itude towards robots. Ta-ble 2 reports the Pearsons correlat ion between the NARS

score of the part icipants and their gaze and ut terance fre-quency and durat ion. The results indicate that the nega-

t ive at t itude does not influence the verbal signal, as there

Fu

ncti

on

al

acce

pta

bil

ity

0

0,225

0,45

0,675

0,9

Robot ics expert ise

0 0,25 0,5 0,75 1

So

cia

l a

cce

pta

bil

ity

0

0,25

0,5

0,75

1

Robot ics expert ise

0 0,25 0,5 0,75 1

0

0,225

0,45

0,675

0,9

N egat ive at t itude towards robot ics

score

0 20 40 60 80

So

cia

l a

cce

pta

bil

ity

0

0,25

0,5

0,75

1

N egat ive at t itude towards robot ics

score

0 20 40 60 80

Fre

que

ncy o

f u

tte

rances

0,05

0,167

0,283

0,4

Extroversion score

55 77 99 121 143 165

y = 0,0009x + 0,1336

Du

ratio

n o

f utt

era

nces

0,05

0,175

0,3

0,425

0,55

Extroversion score

55 77 99 121 143 165

y = 0,0014x + 0,1183

F igur e 3: Scat t er gr aphs show ing t he fr equency(number / seconds) and dur at ion (seconds) of ut t er -

ances of t he par t icipant s (N = 56) , in funct ion of t hei rex t r over sion scor e.

17

4 Publications

Journals

Ivaldi, S.; Anzalone, S.M.; Rousseau, W.; Sigaud, O.; Chetouani, M. (2014) Robot initiative in a team learning task increases the rhythm of interaction but not the perceived engagement.

Frontiers in Neurorobotics. Vol 8, No 5, DOI 10.3389/fnbot.2014.00005

Anzalone, S.M.; Boucenna, S.; Ivaldi, S.; Chetouani, M. (2015) Evaluating the quality of the interaction with social robots. The International Journal of Social Robotics (under revision)

Short papers in International Workshops

Ivaldi, S.; Anzalone, S.; Rousseau, W.; Sigaud, O.; Chetouani, M. (2013). Cues for making a humanoid child more human-like during social learning tasks. Workshop on Towards social

humanoid robots: what makes interaction human-like? - IROS 2013.

Rousseau, W.; Anzalone, S.; Chetouani, M.; Sigaud, O.; Ivaldi, S. (2013). Learning object names through shared attention. Workshop on Developmental Social Robotics - IROS

2013.

Ivaldi, S, ; Anzalone, S.; Rousseau, W.; Sigaud, O.; Chetouani, M. (2014). Robot initiative increases the rhythm of interaction in a team learning task. Workshop Timing in Human-

Robot interaction, in HRI 2014, Bielefeld, Germany.

5 Events

Invited seminars

S. Ivaldi. iCub learning from humans through physical interaction, invited talk in ICRA 2014, Hong Kong, June 2014.

S. Ivaldi. Human-robot interaction with iCub, invited talk at IAS13, Padova, July 2014.

E. Zibetti. (2014): Robotics in Social Cognitive Sciences. A powerful mindtool for studying and improving Human Behavior. Prsentation dans le cadre du "German-French Worskhop

Robotics and Social Sciences": Les ateliers INNOROBO. 20 March 2014. Cite internationale -

Centre de Congres Lyon, France.

M. Chetouani. (2014): Impaired social interaction and robotics. French-German Worskhop Robotics and Social Sciences" : Les ateliers INNOROBO. 20 March 2014. Cite internationale

- Centre de Congres Lyon, France.

S. Ivaldi. Robot learning through human interaction seminar Telecom-ParisTech. Janvier 2014.

S. Ivaldi. Advancements of project EDHHI seminar SENSE project (SMART). December 2013.

18

19

ISMES

20

ISMES Interfaces SensoriMotrices Embarques pour la rducation et la

Supplance

Embedded Sensorimotor Interfaces for rehabilitation and assistance

Responsible of the project:

Agns Roby-Brami [ISIR]

Partners:

ISIR: W. Bachta, N. Jarrass, A. Roby-Brami

STMS: F. Bevilacqua

LIB: V. Marchand-Pauvert, P.F. Pradat, R. Katz, A. Lackmy-Valle

Salptrire Hospital: Physical Medicine and Rehabilitation (P. Pradat-Diehl), neurology (PF. Pradat).

Web site: http://ismes.isir.upmc.fr/

1 The Project at a glance

Context and Objectives

The aim of the project is to study the benefit of techniques associating sensors and effectors-

stimulators that we call sensori-motor interfaces. Those embedded interfaces will allow online

measurements of motor activity and augmented sensory feedback based on a physiological analysis of

human action. Enriched sensory feedback allows to compensate the impairments of sensory loops and

to reinforce the learning of new compensatory actions. The project addresses two scientific challenges:

the first is to establish the necessary models to represent the motor actions in a parsimonious way from

the sensors. The second is to find the efficient encoding of motor behavior to provide pertinent

multisensory signals, easily interpretable by the central nervous system.

The clinical objective is to improve the autonomy of disabled persons thanks to sensori-motor

learning, rehabilitation and assistive technology. For that purpose, our approach is to analyze and

rehabilitate the human activities in an enriched context closer to the daily life activity and to develop

assistive technology as a function of patients' needs. The project is thus closely related to clinical

neuro-rehabilitation.

The multidisciplinary central task of the project is to

develop sensorimotor interfaces for a better analysis of

human motor actions in healthy subjects and neurological

patients. We develop specific interactive devices using

multisensory signals (light touch, vibration, sound) in three

contexts: for a better command of an upper-limb prosthesis

in amputees; for the rehabilitation of gait and posture in

neurological patients (light touch); for the rehabilitation of

arm coordination in stroke patients (coupling gesture-sound).

http://ismes.isir.upmc.fr/

21

2 Scientific progress and results

Work carried out and outcomes achieved over the period

A significant part of the SMART budget is devoted to equipment. We had to acquire the

equipement and to install experimental set-ups, with a special effort to homogeneise protocols between

ISMES laboratories.

A complete set-up for the analysis of hand and finger posture is now operational at ISIR. This

set-up uses data fusion from different sensors (IMU, 6DoF electromagnetic device, dataglove,

force sensors) totally or partially embedded in instrumented objects with an interactive table

(coll. E. Burdet, Imperial College).

We have acquired an up-to-date hand and elbow prosthesis that will be the basis of the

experimental platform for the command of the prosthesis (engineer to hire).

We have acquired a force plateform and duplicated the set up for the analysis of the effect of

light touch on the equilibrium. A similar set-up used at ISIR is now installed in clinical setting

at Salptrire. (Coll A. Wing and R. Reynolds, Birmingham)

We have developed and duplicated a braked elbow orthosis to induce controlable arm

dyscoordination for experimental purpose. This orthesis is fitted with interactive interfaces for

the coupling of movement and sound (Musical objects MO- developed by STMS), visual and

force feedback (ISIR).

We have shared the experience for the extraction and fusion of data and signal processing from

embedded sensors (coll. Popovic, Pavle Savic project).

The internal and external meeting at the beginning of the project SMART were an opportunity to

build or reinforce multidisciplinar collaboration between laboratories (e.g F. Bevilacqua gave a

seminar in LIB).

Manual dexterity and Prosthesis

The analysis of finger coordination for grasping objects

in a bimanual task showed that synergies could be

summarized by 4 Principal Components, with a

particularly good functional correlation with the hand

anatomy (Jarrass et al. 2014). Preliminary experiments

with instrumented objects in healthy subjects and some

stroke patients (Jarrass et al, 2013, Martin et al. in

preparation) allowed to define quantitatively prehension

strategies and to advance in the analysis and

interpretation of IMU signals.

A series of experiments have been performed on the

cortical control of phantom movements made by amputees (coll J. Graaf, ISM). Preliminary

experiments with healthy subjects wearing a fake prosthesis were performed in order to test the effect

of sound feedback on the timing of prehension gestures.

An article has been submitted to question a socio-anthropological approach of prosthesis and corporeal

integration of techniques.

22

Light touch and equilibrium

The effect of light touch on posture has been analysed in healthy

subjects. We have demonstrated that is is possible to drive the

center of pressure in closed loop thanks to a kinesthesic feedback

(Verit et al. 2013, 2014). The use of such a tactile feedback in

neurological patients has been examined and a protocol has been

submitted to a local ethic committee.

In a related study, an active cane has been developed, which is

servo-controlled by the human gait thanks to IMU (Ady et al. 2013).

This will be the basis for the development of the interactive handle

of the cane to provide tactile feedback to the user.

Gesture-sound coupling

The sonification of arm movements is often based on the

movement of one point of the limb (coll. Legos ANR,

STMS). In an aiming experiment we showed the effect of

audiomotor coupling and heading for aiming (Boyer et al.

2013). The challenge for the rehabilitation of shoulder-

elbow coordination in stroke patients is to sonify the

temporal coordination of two variables (e.g. two angles).

Several modes of coupling were developed and tested in

healthy subjects, thanks to the braked orthosis developed in ISIR fitted with musical objects

(Bevilaqua et al. 2013) (see Franoise et al 2014 for another mode of coupling). Further investigations

in healthy subjects were performed and are under analysis before proposing experiments in stroke

patients. More generally, the effect of sound and mechanical constraints (for example those provided

by an exoskeleton, Jarrasse et al in press), should be combined for an efficient rehabilitation.

Preliminary effect of sound was also investigated in the context of hand prosthesis. This project is

complementary of the use of music during gait rehabilitation in Salptrire (V. Marchand Pauvert).

References

Ady R, Bachta W, Bidaud P. (2013) Analysis of cane-assisted walking through nonlinear optimization. Robotics

and Automation ICRA 2013, 3866 3872 Bevilacqua F., Van Zandt-Escobar A., Schnell N., Boyer E. O., Rasamimanana N., Franoise J., Hanneton S.,

Roby-Brami A. (2013) Sonification of the coordination of arm movements. Multisensory Motor Behavior :

Impact of sound . Org Pr A. Effenecberg & Gerd Schmitz, Leibnitz University Hanover. September 2013 Boyer E.O., Babayan BM., Bevilacqua F., Noisternig M., Warusfel O., Roby-Brami A., Hanneton S., Viaud-

Delmon I. (2013) From ear to hand: the role of the auditory-motor loop in pointing to an auditory source.

Front Comput Neurosci. 2013 Apr 22;7:26. doi: 10.3389/fncom.2013.00026.

Franoise J., Schnell N., Bevilacqua F. (2014) MaD: Mapping by Demonstration for Continuous Sonification

ACM SIGGRAPH 2014 Emerging Technologies, Aug 2014, Vancouver, Canada, France. ACM, pp.16:1-16:1

Jarrass N, Kuhne M, Roach N, Hussain A, Balasubramanian S, Burdet E, Roby-Brami A (2013). Analysis of

grasping strategies and function in hemiparetic patients using an instrumented object. Proceedings of the 13th

International Conference on Rehabilitation Robotics (ICORR). Pages 1-8.

Jarrass N, Ribeiro AT, Sahbani A, Bachta W, Roby-Brami A. (2014) Analysis of hand synergies in healthy

subjects during bimanual manipulation of various objects. J Neuroeng Rehabil. 2014 Jul 30;11:113. doi:

10.1186/1743-0003-11-113.

Jarrasse N, Proietti T, Crocher V, Robertson J, Sahbani A, Morel G and Roby-Brami A (2014) Robotic

exoskeletons: a perspective for the rehabilitation of arm coordination in stroke patients. Frontiers in Human

Neuroscience, in press.

Vrit F., Bachta W., Morel G., (2013) Closed-loop control of a human Center-Of-Pressure position based on

somatosensory feedback. IEEE Intelligent Robots and Systems (IROS).

Vrit F., Bachta W., Morel G., (2014) Closed loop kinesthetic feedback for postural control rehabilitation.

IEEE Transactions on Haptics, Special Issue: Haptics in Rehabilitation and Neural Engineering. IEEE Trans

Haptics. 2014 Apr-Jun;7(2):150-60. doi: 10.1109/TOH.2013.64.

23

3 Future Work

As planned, we have acquired the equipment by the end of 2014. The immediate perspective in

2015 is to undertake and complete the experiments (use of human synergies for the control of hand

prosthesis, evaluation of the light touch in neurological patients in Salptrire and sonification of

interjoint coordination). The experiments will be performed thanks to the doctoral students

contributing to the project. The link with the clinics will be developed thanks to C. Kemlin,

physiotherapist in Salpetrire. We shall also hire one engineer who will develop the command of the

hand prosthesis and 2 post doctoral fellows: one will work on the mechanisms of light touch for

assistance, E. Boyer will continue on the sonification project.

We are currently organizing the visit of Pr Archambault, Mc Gill University (in June 2015) by

submitting to invited professors grants. In addition, we plan to be visited by a Spanish doctoral student

for 2 months.

From a dissemination point of view, we plan to organize a workshop on the use of embedded

sensorimotor interfaces for rehabilitation and assistance.

4 Recruitment

Funded by SMART:

Claire KEMLIN, who is a physiotherapist in Salpetrire Hospital: half time in LIB-ISIR for two

years beginning in October 2014 (convention UPMC-APHP)

Ragou ADY, PhD student at ISIR recruited for extra 7 months from Nov 2014 (assistance to

equilibrium)

Jean Baptiste CAZENEUVE mechanical engineer, recruited at ISIR for 2 months (Nov-Dec 2014).

Planned:

Eric Boyer, Post-doc (April-September 2015) in STMS

Engineer 18 months at ISIR beginning spring 2015 (task: prosthesis)

Post-Doc 1 year at ISIR beginning spring 2015 (task: light touch for equilibrium)

Not funded by SMART:

Fabien VRIT: PhD student since 2012 at ISIR (AMN), full time on the project

Manelle MERAD PhD student since 2014 at ISIR (Doctoral school SMAER), full time on the

project

Adrienne GOUZIEN, medical resident in leave (APHP). Contract for 10 months (IUIS UPMC)

Tommaso PROIETTI PhD Student (ISIR, Bourse Ile de France), participating part time

Eric BOYER PhD Student (STMS, ANR LEGOS), participating part time

Jules Franoise, PhD Student (STMS), participating part time

Interns:

Funded by SMART:

Alejandro VAN-ZANDT ESCOBAR (STMS: May-September 2013)

Other funding sources:

Adriano TACILO RIBEIRO, (ISIR) Master 2 ENSTA-UPMC 2013.

Sandra MARTIN, (ISIR) M2 Cogmaster 2013.

Dijana NUIC, (STMS), M2 VHMA 2014.

Wahid TOUNSI (ISIR), M2 2014

24

5 Publications

Journal articles

Boyer E.O., Babayan BM., Bevilacqua F., Noisternig M., Warusfel O., Roby-Brami A.,

Hanneton S., Viaud-Delmon I. (2013) From ear to hand: the role of the auditory-motor loop in

pointing to an auditory source. Front Comput Neurosci. 2013 Apr 22;7:26. doi:

10.3389/fncom.2013.00026.

Vrit F., Bachta W., Morel G., (2014) Closed loop kinesthetic feedback for postural control

rehabilitation. IEEE Transactions on Haptics, Special Issue: Haptics in Rehabilitation and

Neural Engineering. IEEE Trans Haptics. 2014 Apr-Jun;7(2):150-60. doi:

10.1109/TOH.2013.64.

Jarrass N, Ribeiro AT, Sahbani A, Bachta W, Roby-Brami A. (2014) Analysis of hand

synergies in healthy subjects during bimanual manipulation of various objects. J Neuroeng

Rehabil. 2014 Jul 30;11:113. doi: 10.1186/1743-0003-11-113.

Gonzalez, F. and Gosselin, F. and Bachta, W. (2014). Analysis of Hand Contact Areas and

Interaction Capabilities During Manipulation and Exploration. IEEE Transactions on

Haptics. In press.

Conferences

Vrit F., Bachta W., Morel G., (2013) Closed-loop control of a human Center-Of-Pressure

position based on somatosensory feedback. IEEE Intelligent Robots and Systems

(IROS).

Bevilacqua F., Van Zandt-Escobar A., Schnell N., Boyer E. O., Rasamimanana N.,

Franoise J., Hanneton S., Roby-Brami A. (2013) Sonification of the coordination of arm

movements. Multisensory Motor Behavior : Impact of sound . Org Pr A. Effenecberg

& Gerd Schmitz, Leibnitz University Hanover. September 2013

Roby-Brami A., Van Zandt-Escobar A., Jarrass N., Robertson J., Schnell N., Boyer E. O.,

Rasamimanana, Hanneton S., Bevilacqua F. (2014) Toward the use of augmented auditory

feedback for the rehabilitation of arm movements in stroke patients. 17th European

congress of physical rehabilitation medicine. Marseille May 2014.

Franoise J., Schnell N., Bevilacqua F. (2014) MaD: Mapping by Demonstration for

Continuous Sonification ACM SIGGRAPH 2014 Emerging Technologies, Aug 2014,

Vancouver, Canada, France. ACM, pp.16:1-16:1

Ady R., Bachta W., Bidaud, P. (2014). Development and control of a one-wheel telescopic

active cane. IEEE RAS/EMBS BioRob Pages 461 466

Other related publications (SMART not acknowledged)

Jarrass N, Kuhne M, Roach N, Hussain A, Balasubramanian S, Burdet E, Roby-Brami A

(2013). Analysis of grasping strategies and function in hemiparetic patients using an

instrumented object. Proceedings of the 13th International Conference on

Rehabilitation Robotics (ICORR). Pages 1-8.

Ady R, Bachta W, Bidaud P. (2013) Analysis of cane-assisted walking through nonlinear

optimization. Robotics and Automation ICRA 2013, 3866 3872.

Jarrasse N, Proietti T, Crocher V, Robertson J, Sahbani A, Morel G and Roby-Brami A

(2014) Robotic exoskeletons: a perspective for the rehabilitation of arm coordination in

stroke patients. Frontiers in Human Neuroscience, in press.

http://www.isir.upmc.fr/?op=view_profil&lang=fr&id=294

25

6 Events

Workshop "Intgration corporelle de la technique" (Body integration of technic) Dfi-sens

CNRS and ISCC http://ict2012.isir.upmc.fr/ in 2012.

Participation to "LEGOS days" ANR project STMS, 18-19 March 2014

Seminars

Milica Djuric-Jovicic (Belgrade university) ISIR 20/06/2013

F. Bevilaqua, LIB (Decembre 2013).

Hugh Herr (MIT), ISIR 13/6/2014 "On the Design of Bionic Leg Devices: The Science of

Extreme Interface".

Meeting with C. Lenay and O. Gapenne (UTC) April 2014.

Meeting with J. Mizrahi (Technion) ISIR 5 March 2014

Internal events

Kick off- meeting: 21/11/2013

Meeting: 12/09/2014

General audience events

N. Jarrass : participation to the documentary "Bras de fer" for the series "LA BOITE

NOIRE"

Roby-Brami in La Grande quation , Normand Mousseau (Universit de Montral)

Broadcast radio Ville-Marie (Qubec), April 2014

N. Jarrass: RFI Broadcast "Autour de la question" November 2014 "Quel dfi pour

l'humain avec les technosciences ?"

N. Jarrass: Participation to Semaines Sociales de France, Lille Nov 2014 "l'homme et les

technosciences: le defi".

http://ict2012.isir.upmc.fr/

26

27

ONBUL

28

ONBUL: Online Budgeted Learning Responsible of the project: Ludovic DENOYER [LIP6]

Partners:

LIP6: Ludovic DENOYER, Patrick GALLINARI, Gabriella CONTARDO

ISIR: Benoit GIRARD, Mehdi KHAMASSI, Nassim AKLIL

Web site: http://onbul.lip6.fr

1 The Project at a glance

Context and Objectives

The emergence of large-scale databases and big data has recently motivated the development of

budgeted machine learning models able to learn under operational constraints in term of memory/CPU

consumptions, data access, etc. It involves integrating the constraints of scarce resources in the

learning process itself. In parallel, in the neuroscience community, reinforcement learning online

capabilities are understood as results of the coexistence of complementary learning systems, the choice

of which brain learning system should be activated at each moment being also based on limited

budgets (mainly in terms of computational cost). Based on the observation that the recent context of

learning under stress is highly relevant both for massive data processing and neuroscience, we aim to

study this issue in the online learning context that seems suited for these two families of problems. We

seek cross-fertilization between the two fields: 1) import the multi-model (i.e. multiple learning

systems) from neuroscience in statistical machine learning architectures as a potential solution to

budgeted data analysis and prediction; 2) update the reinforcement learning concepts in use in

neuroscience by a confrontation with modern budgeted learning frameworks.

The project is organized around scientific objectives and different concrete applications. The

scientific objectives are to develop original budgeted learning models. We will explore two families of

models: a first family where the information acquisition process will be modeled as a sequential

process, and where reinforcement learning and representation learning techniques will be used

together. The second family that is more human-inspired aims at developing model selection

approaches where, at each step of the sequential process, the system has to choose between different

concurrent decision/learning models, each model having its own prediction/learning ability, but also

its own budget. These families of models will be both explored when the budget applies in prediction

only, but also when the budget applies during learning, resulting in online budgeted learning models.

Experiments will be made on classical machine learning models involving large amount of data

(recommendation, image classification), in robotics (robot localization), and also in the

neuroscience domain (modeling behavioral data).

2 Scientific progress and results

During the first year of the project, we have mainly focused on the first family of models i.e.

sequential budgeted models as described in Section 2.1. This has been both investigated in terms of

big data processing (LIP6) and through the set up of a new robotic paradigm in collaboration between

the two partners in order to have a robotic automatically collect information under budget constraints

for localization. The development of model selection approaches (Section 2.2) started this year. It

involved first investigations of computational principles from neuroscience which best explain how

animal behavior relies on budget constraints to perform online model selection and learning

parameters regulation (meta-learning). The second part of model selection approaches will start next

http://onbul.lip6.fr/

29

year and will again involve collaboration between the two partners to have a robot perform online

model selection under budget constraints during a navigation task.

2.1 Sequential Budgeted Learning

Existing machine learning approaches are based on the following strong assumptions: (i) It is

assumed that data to be processed is fully known, that is to say that needed information was previously

acquired. (ii) It is also assumed that the system is not constrained in terms of inference or learning

time, memory space,... However, these assumptions are unrealistic in the emerging eco-system: (i) The

usual paradigm which considers that the learning method has to optimize a single task-dependent

performance without taking into account the time taken to learn, the complexity of the produced

method, or even its CPU/Memory consumption is outdated. (ii) Considering that information has been

already acquired and stored in a Big Data context is unrealistic since the amount of produced data is so

huge that, in the best case, it can only be stored through very large expensive storage clusters, and in

many cases, it cannot be stored at all. The information acquisition process is thus a key element in

machine learning, which is today only done by hand. The ability of a system to automatically

determine which information has to be collected but also to realize a good balance between

performance and operationability. This ability is thus a key aspect of future machine learning

models that we started to develop in this project.

2.1.1 Principles

The project aims at studying the following process in a massive data context: (i) first a model has to

choose which information to use. This first phase is called information acquisition and is a critical

point. Collecting misleading or irrelevant information will both decrease the ability of the system to

solve a particular task and increase its budget i.e. collecting information can be time/memory-

consuming, or even expensive. This acquisition process occurs during both the learning phase where

one wants to build a ''good'' training set, and during the testing phase where one wants to predict

outputs. In the first case, the model has to learn which information, but also which supervision, to get,

while in the second case the model has to acquire the information that will help it to do a good

prediction. (ii) At each step of the process, the collected information has to be aggregated. This phase

called representation learning - which has seen a surge of interest during the last year with the

emergence of the representation learning community - aims at ''aggregating'' the collected data, and

extracting relevant information on which learning/inference will be done. (iii) At last, the system has

to perform a prediction. During the learning process, this last step aims at producing or updating a

model, while during the testing phase; it aims at producing an output for a given datum. Note that,

since the information acquisition will be guided by previously acquired information, step (i) and (ii)

can be inter-dependent. It can be instantiated both during learning and during inference. At last, in

order to deal with large scale datasets, these three steps have to be jointly constrained by budgeted

constraints like time spent, CPU consumption, memory usage,...

Positioning w.r.t state-of-the-art

2.1.2 Main achievements Considering the previous description of what we intend to realize, we have already proposed some

original approaches to different aspects of this proposal:

30

The development of sequential acquisition models has been handled by developing specific

approximated reinforcement learning models. The underlying idea is to model the

acquisition process by a Markov Decision Process where each action can be either an

acquisition action or a decision action allowing the model to choose if it needs to get more

information, or if it can decide what to predict. We have proposed a new reinforcement-

learning algorithm for the case where the number of acquisition steps is fixed and cannot be

exceeded. Applications on image classification, where the classification model sequentially

explore parts of the image have been proposed.

We have also proposed original representation learning algorithms for states in a partially

observed Markov Decision Process (POMDP). When facing approximated reinforcement

learning problems, the input consists in a feature vector - called observation - which is

assumed to fully characterize the current state of the process, thus allowing for an optimal

action choice. However, this assumption is unrealistic in real-life applications where the

observation is only a partial view of the current state provided by limited sensors. The model

operates in two steps. (i) First, it learns how to find good representations on a set of randomly

collected trajectories. This unsupervised operation is used to learn the system only once, and

may be used to tackle different tasks sharing the same dynamical process. (ii) The model then

infers new representations for any new trajectory, these representations being then used for

discovering an optimal policy for a particular reward function.

2.1.3 Work in progress

We are currently trying to merge the two approaches by developing (sequential) models able to

simultaneously learn a representation of the acquired information, but also how/when to acquire

information and when/how to predict. A first model which is not sequential is currently under

development and is showing promising results. It is based on a L1-regularization technique where the

L1-regularized weights are not applied on the parameters of the model, but on the information that can

be acquired: a zero-weight on a particular input means that this information is not needed for

computing a good prediction. A sequential extension of this model is also under development.

Submissions are planned to both ICLR 2015 and ICML 2015. Concrete experiments have been made

on different types of data: (i) image classification (ii) toys MDPs (iii) recommender systems.

2.2 Model selection approaches Work on model selection is described in more details in the PhD report joined to this report.

In the last 15 years, the theory of reinforcement learning has significantly contributed to researches in

machine learning, robotics and neuroscience. It formally specifies how an agent should choose the best

actions to perform and update this choice through learning by trial-and-error so as to maximize long-

term cumulative rewards. This theory has helped better understand the mechanisms underlying

reinforcement-based plasticity in brain circuits dedicated for action selection. In parallel, it contributed

in designing adaptive agents that can learn optimal paths to rewards in simulated discretized grid

worlds. However, the application of reinforcement learning algorithms to robotics experiments

involving continuous noisy unpredictable environments - produced limited progresses, due to

applications to quite simple problems, with a small number of states and actions, to slowness in

learning and to systematic instability observed throughout the learning process. This led to the idea

that an online dynamic regulation of reinforcement learning algorithms was necessary to produce

efficient and robust robotics results. Such online regulation is called meta-learning and consists in two

main processes:

(i) The online regulation of reinforcement learning parameters (e.g. the exploration parameter) so

that they are not constant over time but are rather dynamically adapted to the current task

requirements and performance of the agent. For instance, if the agent detects that its

performance is getting worse, this may indicate that a change in the task has occurred and that

31

the agent should change its parameter for exploration so as to re-explore and re-learn the new

task contingencies.

(ii) The online selection between learning models through the monitoring of the agents

performance: each model having its own advantages and drawbacks, the agents should be able

to learn which model is the most appropriate for each subpart of the learning process. Meta-

learning algorithms have been recently applied to online learning problems both to the

Neuroscience and Robotics. However, they do not explicitly take into account the budget

constraint: how much time and computational cost the agent can use to learn the task? On the

other hand, budgeted learning methods have been proposed in machine learning, but they do

not yet work online. The objective of this work is thus to extend budgeted learning methods to

make them work online by taking inspiration from recent neuroscience data, and to apply them

to online meta-learning and model selection tasks in Robotics. The initial criterion that was

used at ISIR for online model selection consisted in learning in a meta-controller which

learning model among two was the most efficient at each moment. The two tested learning

models were a model-based system that learns progressively the possible transitions between

states in the environment; the second tested model was a model-free system that avoids

learning transitions and rather simply learns reward values associated to each possible action

in each possible state. We have previously tested such algorithm in navigation in a simple

robotic task. The robot could efficiently but slowly adapt to changes in the goal location by

adapting its model selection.

The objective of the current investigations is to explicitly take into account the computational cost

(budget) of each model so as to perform online model selection as a function of this cost. This should

reduce the learning cost and should push the algorithm to learn the task in a shorter time. On the other

hand, in uncertain situations following a change in the environment, such an algorithm should be able

to detect that a high cost is necessary to adapt to the new situations by performing computations in

more than one model and analyzing which model is the most efficient in this new situation.

3 Future Work

The sequential budgeted machine learning models will be finished during the first half of 2015 with

the development of sequential representation learning models and their applications to collaborative

filtering. The second half of the year will be devoted to both the development of learning models with

a large number of actions which is a needed condition to large scale budgeted acquisition and to

the development of models able to learn under budgeted constraints.

Concerning the robotics task of automatic navigation, ongoing work on budgeting sensors will be

completed during the first third of 2015. The rest of the year will be devoted to the development of the

learning online budgeted algorithm aiming at selecting between navigation strategies depending on

their respective effectiveness and computational costs.

4 Recruitment

In addition to permanent researchers, the project currently involves two PhD students:

Nassim Aklil at ISIR who mainly works on applying sequential budgeted learning models to

robot, and who is starting to explore model selection approaches to online budgeted learning

Gabriella Contardo at LIP6 (grant not provided by the LABEX) who works on sequential

budgeted learning models applied to big data tasks.

A postdoc will be recruited in 2015 for the specific development of online sequential budgeted

algorithms.

32

5 Publications

Contardo G., Denoyer L., Artires T., Gallinari P. (2014) Learning States Representations

in POMDP. CoRR abs/1312.6042 (2013) and ICLR 2014 (Short paper)

Contardo G., Denoyer L., Artires T., Gallinari P. (2014) Apprentissage Sous Contraintes

Budgetises Application la Recommendation Poster CAP 2014

Contardo G., Denoyer L., Artires T., Gallinari P. (2014): Apprentissage Sous Contraintes

Budgetises Application la Recommendation Poster CAP 2014

Dulac-Arnold G., Denoyer L., Thome N., Cord M., Gallinari P. (2014) Sequentially

Generated Instance-Dependent Image Representations for Classification, Internation

Conference on Learning Representations ICLR 2014

Aklil N., Marchand A., Fresno V., Coutureau E., Denoyer L., Girard B., Khamassi M.

(2014) Modelling rat learning behavior under uncertainty in a non-stationary multi-armed

bandit task. Fourth Symposium on Biology of Decision Making (SBDM 2014). Paris.

Denoyer L., Gallinari P. (2014) Deep Sequential Neural Network (2014) - Workshop

Deep Learning NIPS 2014

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PhD Thesis: Navigation learning with multiple models under budgeted

constraints PhD student: Nassim Aklil

Supervisor(s): Mehdi Khamassi (ISIR) & Ludovic Denoyer (LIP6)

Laboratory: Institute of Intelligent Systems and Robotics (ISIR, UPMC-CNRS)

Doctoral School: ED3C, Cerveau Cognition Comportement (UPMC)

Period: 01/09/2013 31/08/2016

1 Description

In the last 15 years, the theory of reinforcement learning has significantly contributed to

researches in machine learning, robotics and neuroscience. It formally specifies how an agent should

choose the best actions to perform and update this choice through learning by trial-and-error so as to

maximize long-term cumulative rewards. This theory has helped better understand the mechanisms

underlying reinforcement-based plasticity in brain circuits dedicated for action selection. In parallel, it

contributed in designing adaptive agents that can learn optimal paths to rewards in simulated

discretized grid worlds. However, the application of reinforcement learning algorithms to robotics

experiments involving continuous noisy unpredictable environments - produced limited progresses,

due to applications to quite simple problems, with a small number of states and actions, to slowness in

learning and to systematic instability observed throughout the learning process. This led to the idea

that an online dynamic regulation of reinforcement learning algorithms was necessary to produce

efficient and robust robotics results. Such online regulation is called meta-learning and consists in two

main processes:

1. The online regulation of reinforcement learning parameters (e.g. the exploration parameter) so

that they are not constant over time but are rather dynamically adapted to the current task

requirements and performance of the agent. For instance, if the agent detects that its

performance is getting worse, this may indicate that a change in the task has occurred and that

the agent should change its parameter for exploration so as to re-explore and re-learn the new

task contingencies.

2. The online selection between learning models through the monitoring of the agents

performance: each model having its own advantages and drawbacks, the agents should be able

to learn which model is the most appropriate for each subpart of the learning process.

Meta-learning algorithms have been recently applied to online learning problems both to the

Neuroscience and Robotics. However, they do not explicitly take into account the budget constraint:

how much time and computational cost the agent can use to learn the task? On the other hand,

budgeted learning methods have been proposed in machine learning, but they do not yet work online.

The objective of this PhD thesis is thus to extend budgeted learning methods to make them work

online by taking inspiration from recent neuroscience data, and to apply them to online meta-learning

tasks in Robotics.

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2 Results

Two experimental works have been performed so far:

1. We investigated neuroscience data illustrating meta-learning processes during the online

regulation of exploration in rats having to learn a multi-arm bandit task under different levels

of uncertainty (Figure 1 Left).

2. We prepared a robotics setup to apply online budgeted learning in an initial simple navigation

task. The goal of the robot is to minimize the budget (here the number of accessed data about

the environment from its sensors) in order to determine its current position (Figure 1 Right).

FIGURE 1: (LEFT) ONLINE REGULATION OF EXPLORATION (META-LEARNING) IN A RAT MULTI-ARMED BANDIT TASK. RATS

HAVE TO CHOOSE AT EACH TRIAL BETWEEN 3 LEVELS, EACH ONE BEING ASSOCIATED WITH A DIFFERENT PROBABILITY OF

REWARD. MODEL FITTING ON RAT BEHAVIOR USING A META-LEARNING ALGORITHM REVEALED THAT THEY DYNAMICALLY

REGULATE THEIR EXPLORATION LEVEL IN ORDER TO EFFICIENTLY SOLVE THIS TASK. (RIGHT) ROBOTICS NAVIGATION SETUP

TO INVESTIGATE ONLINE BUDGETED LEARNING FOR THE DETERMINATION OF THE ROBOTS LOCATION. THIS PART OF THE

WORK HAS JUST STARTED AND WE HAVE MADE THE SPECIFICATION AND STARTED THE ACQUISITION OF A DATASET WITH

DIFFERENT ROBOT POSITIONS, SENSING DATA AND MOVEMENTS.

3 Publication

Aklil N., Marchand A., Fresno V., Coutureau E., Denoyer L., Girard B., Khamassi M. (2014) Modelling rat learning behavior under uncertainty in a non-stationary multi-armed bandit task.

Fourth Symposium on Biology of Decision Making (SBDM 2014). Paris.

35

SeNSE

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SeNSE: Socio-Emotional Signals Responsible of the project: Catherine ACHARD

Partners:

ISIR (UPMC): C. Achard, K. Bailly, M. Chetouani, S. Dubuisson, O. Grynzspan

LIP6 (UPMC): P. Garda, C. Marsala, A. Pinna, M. Rifqi

LTCI (Telecom Paris-Tech): C. Clavel, S. Essid, C. Pelachaud, G. Richard

STMS (IRCAM): F. Bevilacqua, G. Assayag

Web site: http://sense.isir.upmc.fr/en/

1 The Project at a glance

Context and Objectives

The SeNSE project, which focuses on social emotional signals exchanged during an

interaction, investigates topics ranging from signal acquisition (video, audio, neurophysiological) to

interaction (virtual agent, musical interaction, people interaction) including interpretation and

modeling. This project brings together key partners of social signal processing, machine learning,

electronics and human computer interaction. The SeNSE project will break new ground for the multi-

modal analysis and synthesis of social behavior. We are particularly interested in both dynamical and

temporal aspects of interaction.

The methodology will deal the heterogeneous nature of cues from low-level information (audio, video,

EEG, ECG, EDA) to high-level information (emotions, social attitude, user traits). Thus, the

considered signals are multimodal with their own dynamics and they may influence each other during

social interactions. For example, for a virtual agent, understanding the dynamics of socio-emotional

signals is one of the challenges for the analysis and synthesis of realistic behaviors. In musical

interaction, analyzing and describing multi-modal signals in large group provide new paradigms for

expressive and collaborative interactions.

Analyzing such situations by social signal processing techniques requires new models and

methodologies. To tackle this challenging problem, we focus on three main aspects (1) developing

computational models of socio-emotional behaviors considering different modalities (audio, gestural,

http://sense.isir.upmc.fr/en/

37

physiological and brain signals...) (2) studying intermodal and temporal dependencies for both intra

and inter personal signals and (3) designing smart devices embedding socio-emotional processing.

2 Scientific progress and results

The first two main aspects of SeNSE have been studied during the first 18 months (total duration 48

months).

2.1. MULTI-MODAL MODELS FOR SOCIO-EMOTIONAL BEHAVIOR

In the literature, low-level information have been considered to study social interactions by

mainly exploiting audio, video or either physiological signals... The SeNSE project investigates these

various multi-modal signals in order to build rich computational models of socio-emotional behaviors.

The aim of physiological study is to design a smart sensor with an embedded adaptive emotion

recognition algorithm. For this purpose, a set of experimental physiological data has been recorded

during the last months. We considered a person watching a football game during the world cup soccer

in Brazil and we recorded skin conductivity, respiratory and ECG signals. The aim is to map features

extracted from physiological signals to games events such as goal against the supported team, of the

supported team, corner, and free kick We expect that these events will elicit natural emotions will,

which will allow us to infer relevant features for automatically recognizing emotion from

physiological sensors. The objective is to design electronic sensors able to embed an adaptive

recognition algorithm from these cues. The rationale here is to develop systems that are able to adapt

in real-time, while ensuring privacy of data (all data are acquired and processed by the same device).

A PhD supervised by M. Rifqi, A. Pinna, P. Garda and C. Marsala started in November 2014 on

this issue.

Regarding audio-visual signals, we focused on vocal and facial features corresponding to

social attitudes. We have provided a preliminary state-of-the-art on the various definitions and theories

associated with social attitudes and studied the available annotations of the SEMAINE Database.

Prosodic cues and action units have been extracted on this database. As annotations of social stances

are not provided in SEMAINE, we have focused on the study of their correlation with emotional

valence, as a first step. Future work will focus to the development of a database dedicated to the

analysis of social stances (e.g. dominance and friendliness) in a human-agent interaction relying on

SEMAINE protocols and the analysis of intermodal dependencies with the aim to make the agent able

to express social stances through prosodic and facial expression cues. A PhD supervised by K.

Bailly, C. Clavel and G. Richard started in October 2014 on this issue.

2.2. MODELING TEMPORAL DEPENDENCIES OF INTER AND INTRA INDIVIDUAL BEHAVIOURS

We study the modeling of temporal dependencies of features and behaviors at inter and intra

personal levels through various machine learning approaches (influence models, non-negative matrix

factorization, one-class SVMs) and various interactive situations (imitation, meeting, social agent,

musical interaction...).

Influence Models (IM) were used to model turn-taking in a meeting of 4 persons. Rather than

using the IM as classifier, the influence matrix is estimated and employed to characterize meetings.

The influence matrix is then used as a feature input of an SVM classifier. Preliminary results show the

interest of this characterization for a role recognition task. A PhD supervised by C. Achard and S.

Dubuisson will start in January 2015 on this issue.

We study co-factorization of non-negative matrices for EEG-based characterization of specific

forms of interaction between two individuals, such as imitation. Efforts have been dedicated to the

estimation of appropriate NMF models, in particular considering co-factorization schemes, whereby

the NMF models for the two subjects are estimated jointly in a coupled fashion. Preliminary results are

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encouraging because some levels of correlation between NMF activations relating to each subject

during imitation phases were measured. A PhD supervised by S. Essid and M. Chetouani started in

October 2014 on this issue.

In another context, we investigated automatic measurement of imitation in a dyad.

Participants gestures during an EEG hyper-scanning task are characterized with 1-class SVM models.

Then a measure of imitation is derived from the likelihood ratio between these models. The

comparison with manual indexing validates the method at both behavioral and neural levels,

demonstrating its ability to discriminate significantly the periods of imitation and non-imitation during

social interaction.

Finally, the timing of a specific non-verbal social behavior, that is, communicative gaze, has

also been investigated. One of the most crucial gaze communicative actions is gaze following, i.e.

when a social partner follows with her/his eyes the gaze of the other partner. The goal was to examine

what is the acceptable delay between the eye movements of the partners for them to be engaged in

gaze following behaviors. An experiment was conducted, where participants were asked to judge

whether the gaze of a virtual human avatar responded to their gaze or not.

All previous studies have focused on short-term social cues dependencies, but in order to adapt

the interaction according to the users, a longer-term study is needed. Thus, by addressing two different

use cases, Embodied Conversational Agents and Musical Improvisation Agents, we aimed at building

a generic adaptive interaction model that should avoid the shortcoming of using ad-hoc rules. In

particular, the long-term model should allow modulating short-term dependencies such as turn-taking

and the synchronization of non-verbal elements.

During the first 18-month of the project, we started to establish a state-of-the art of adaptive

interaction models that could fit our goals. Several disciplines have been covered from Embodied

Conversational Agents, Musical Interactions and Music improvisation or Human-Robot interaction.

From a modeling point of view, very different architectures are currently reviewed, such as rule-based

systems, probabilistic models (e.g. HMM) or autonomous agents. A PhD supervised by F.

Bevilacqua and C. Pelachaud is currently working towards the design of interaction scenarios to

compare various approaches on this issue.

3 Future Work

In 2015, we will focus on data collection. This will be performed in two steps: (1) analysis of

the state-of-the-art on databases for social interactions investigations (meetings, presentations, data

shared during challenges), (2) identification of proof-of-principle situations for the PhD thesis (e.g.,

inter-brain synchrony characterization, social stances modeling, and musical interactions).

Regarding the computational models, we will develop and evaluate models dealing with (1)

emotional behaviors and (2) temporal dependencies of intra and inter individual behaviors. These will

be performed for analysis, modeling and synthesis phases in human-human and human-virtual agent

situations.

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4 Recruitment

INTERNSHIPS

Study of multimodal synchrony during social interaction, F. Aujoux, M2 Internship (march-july 2014). Supervisors: C. Achard (ISIR), S. Dubuisson (ISIR)

Modeling the temporality of multimodal social cues exchanged during natural interactions, S. Fang, M2 Internship (july-december 2014). Supervisors: C. Achard (ISIR), S. Dubuisson (ISIR)

Multimodal analysis and recognition of social signals, L. Chen, M2 Internship (march-august

2014). Supervisors: C. Clavel (LTCI-CNRS) and K. Bailly (ISIR)

Study of the judgment of agency in the gaze modality, S. Recht, M1 Internship (March-June

2014). Supervisor: O. Grynszpan (ISIR)

Modeling of neurophysiological activity related to the dynamics of an interaction with latent

variables analysis, A. Hajlaoui, M2 Internship (april-august 2014). Supervisor: S. Essid (LTCI-

CNRS), M. Chetouani (ISIR)

PHD THESIS

Embedded architecture and physiological sensors, W. Yang, 2014-2017. Supervisors: C. Marsala (LIP6), M. Rifqi (LIP6) and A. Pinna (LIP6)

Multimodal analysis and recognition of social signals: application to social stance generation

in virtual agents, T. Janssoone, 2015-2018. Supervisors: G. Richard (LTCI-CNRS), C. Clavel

(LTCI-CNRS) and K. Bailly (ISIR)

Study of social cues exchanged during natural interactions, S. Fang, 2015-2018, Supervisors:

C. Achard (ISIR) and S. Dubuisson (ISIR)

Temporal Adaptation of Interaction, Kevin Sanlaville, 2013-16. Supervisors: C Pelachaud

(LTCI-CNRS), F. Bevilacqua (STMS) , G. Assayag (STMS)

Modeling interactional neurophysiological activity using latent variables, A. Hajlaoui, 2014-

2017, Supervisors: M. Chetouani (ISIR) and S. Essid (LTCI-CNRS)

5 Publications

S. Buisine, M. Courgeon, A. Charles, C. Clavel, J.C. Martin, N. Tan, O. Grynszpan, The Role of Body Postures in the Recognition of Emotions in Contextually Rich Scenarios,

International Journal of Human-Computer Interaction, 30 (1), 2014

S. Campano, J. Durand, C. Clavel, Comparative analysis of verbal alignment in human-human and human-agent interactions, In Proceedings of LREC 2014, Reykjavik

S. Campano, N. Glas, C. Clavel, C. Pelachaud, Production d'Hetero-Rptition chez un ACA, In Proc. Workshop Affect, Compagnon Artificiel, Interaction, 2014

M. Chetouani, Role of Inter-Personal Synchrony in Extracting Social Signatures: Some Case Studies, International Workshop on Roadmapping the Future of Multimodal Research, in

conjunction with the ACM International Conference on Multimodal Interaction

(ICMI'14), Istanbul, Turkey, November 2014.

M. Courgeon, G. Rautureau, J.C. Martin, O. Grynszpan, Joint Attention Stimulation using Eye-Tracking and Virtual Humans, IEEE Transactions on Affective Computing, July 2014

E. Delaherche, G. Dumas, J. Nadel, M. Chetouani, Automatic measure of imitation during social interaction: a behavioral and hyperscanning-EEG benchmark, Pattern Recognition

Letters, to appear

C. Langlet and C. Clavel, Modlisation des questions de lagent pour lanalyse des affects, jugements et apprciations de lutilisateur dans les interactions humain-agent, In Actes de

TALN 2014, Marseille

C. Langlet, C. Clavel, Modelling user's attitudinal reactions to the agent utterances: focus on the verbal content, LREC Workshop on Emotion, social signals, sentiment & linked open

data, May 2014

http://www.lrec-conf.org/proceedings/lrec2014/pdf/327_Paper.pdfhttp://www.lrec-conf.org/proceedings/lrec2014/pdf/327_Paper.pdf

40

S. Michelet, C. Achard, M. Chetouani, Evaluation automatique de l'imitation dans l'interaction, Reconnaissance de Formes et Intelligence Artificielle (RFIA 2014).

K. Sanlaville, F. Bevilacqua, C. Pelachaud, G. Assayag, Adaptation in an Interactive Model designed for Human Conversation and Music Improvisation: a preparatory outline, Workshop

Affect, Compagnon Artificiel Interaction (WACAI1), 2014, Rouen

6 Events

SEMINARS

Automatic Recognition of Affective and Social Signals Hatice Gunes, from Queen Mary University, Londres,

Tlcom-ParisTech, the 10/09/14

Understanding conversational social video Daniel Gatica-Perez, from IDIAP, EPFL

ISIR, UPMC, the 09/10/2013

L'interaction spontane chez l'homme: neuroimagerie et modles computationelles Guillaume Dumas, from Institut du cerveau et de la moelle piniaire

ISIR, UPMC, the 02/10/2013

The MEI Robot: Towards Using Motherese to Develop Multimodal Emotional Intelligence Angelica Lim, from Okuno Speech Media Processing Lab

ISIR, UPMC, the 27/09/2013

SPECIAL SESSION

Special session on Behavior Imaging at the IEEE International Conference on Image

Processing (ICIP), October 2014.

Organizers: Sverine Dubuisson (ISIR), Jean-Marc Odobez (IDIAP), Mohamed Chetouani (ISIR)

Human behavior understanding using both computer vision and signal processing has become of

major interest since the emergence of numerous applications in various domains, such as social signal

processing, affective computing or human-computer interaction. Recent advances in computer vision,

signal processing and pattern recognition now make it possible to consider the development of tools or

systems for human-human interaction or human-computer interaction analysis.

In line with these current efforts, behavior imaging was first introduced in the context of behavioral disorder monitoring (e.g. autism). The key concept of behavior imaging is to improve,

through interdisciplinary approaches, automatic computing methods with the long-term goal of

enhancing human behavior analysis.

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Phd Thesis: Study of social cues exchanged during natural interactions PhD student: Sheng FANG

Supervisor(s): Catherine Achard and Sverine Dubuisson

Laboratory: ISIR

Doctoral School: SMAER

Period: 01/01/2015 31/12/2017

1 Description

Recent researches in cognitive science define social intelligence as the ability to express and

recognize social signals produced during natural social interactions such as politeness, empathy,

kindness, conflict, etc., coupled with the ability to engage and maintain an interaction. A new

discipline is emerging: social signal processing, which aims to understand and model social

interactions (for human sciences, social sciences, and communication sciences), and provides similar

capabilities to computers (for human/computer interaction, animation of avat