2023Activity reportProject-TeamLARSEN

Scientific context

So far, only a few autonomous robots have been deployed for a long time (weeks, months, or years) outside of factories and laboratories. They are mostly mobile robots that simply “move around” (e.g., vacuum cleaners or museum “guides”) and data collecting robots (e.g., boats or underwater “gliders” that collect data about the water of the ocean).

A large part of the long-term autonomy community is focused on simultaneous localization and mapping (SLAM), with a recent emphasis on changing and outdoor environments 42, 52. A more recent theme is life-long learning: during long-term deployment, we cannot hope to equip robots with everything they need to know, therefore some things will have to be learned along the way. Most of the work on this topic leverages machine learning and/or evolutionary algorithms to improve the ability of robots to react to unforeseen changes 42, 48.

Main challenges

The first major challenge is to endow robots with a stable situation awareness in open and dynamic environments. This covers both the state estimation of the robot by itself as well as the perception/representation of the environment. Both problems have been claimed to be solved but it is only the case for static environments 47.

In the Larsen team, we aim at deployment in environments shared with humans which imply dynamic objects that degrade both the mapping and localization of a robot, especially in cluttered spaces. Moreover, when robots stay longer in the environment than for the acquisition of a snapshot map, they have to face structural changes, such as the displacement of a piece of furniture or the opening or closing of a door. The current approach is to simply update an implicitly static map with all observations but without attempt at distinguishing the suitable changes. For localization in not-too-cluttered or not-too-empty environments, this is generally sufficient since a significant fraction of the environment should remain stable. But for life-long autonomy, and in particular for navigation, the quality of the map, and especially the knowledge of the stable parts, is primordial.

A second major obstacle to moving robots outside of labs and factories is their fragility: Current robots often break in a few hours, if not a few minutes. This fragility mainly stems from the overall complexity of robotic systems, which involve many actuators, many sensors, and complex decisions, and from the diversity of situations that robots can encounter. Low-cost robots exacerbate this issue because they can be broken in many ways (high-quality material is expensive), because they have low self-sensing abilities (sensors are expensive and increase the overall complexity), and because they are typically targeted towards non-controlled environments (e.g., houses rather than factories, in which robots are protected from most unexpected events). More generally, this fragility is a symptom of the lack of adaptive abilities in current robots.

Angle of attack

To solve the state estimation problem, our approach is to combine classical estimation filters (Extended Kalman Filters, Unscented Kalman Filters, or particle filters) with a Bayesian reasoning model in order to internally simulate various configurations of the robot in its environment. This should allow for adaptive estimation that can be used as one aspect of long-term adaptation. To handle dynamic and structural changes in an environment, we aim at assessing, for each piece of observation, whether it is static or not.

We also plan to address active sensing to improve the situation awareness of robots. Literally, active sensing is the ability of an interacting agent to act so as to control what it senses from its environment with the typical objective of acquiring information about this environment. A formalism for representing and solving active sensing problems has already been proposed by members of the team 41 and we aim to use it to formalize decision-making problems for improving situation awareness.

Situation awareness of robots can also be tackled by cooperation, whether it be between robots or between robots and sensors in the environment (led out intelligent spaces) or between robots and humans. This is in rupture with classical robotics, in which robots are conceived as self-contained. But, in order to cope with as diverse environments as possible, these classical robots use precise, expensive, and specialized sensors, whose cost prohibits their use in large-scale deployments for service or assistance applications. Furthermore, when all sensors are on the robot, they share the same point of view on the environment, which is a limit for perception. Therefore, we propose to complement a cheaper robot with sensors distributed in a target environment. This is an emerging research direction that shares some of the problematics of multi-robot operation and we are therefore collaborating with other teams at Inria that address the issue of communication and interoperability.

To address the fragility problem, the traditional approach is to first diagnose the situation, then use a planning algorithm to create/select a contingency plan. But, again, this calls for both expensive sensors on the robot for the diagnosis and extensive work to predict and plan for all the possible faults that, in an open and dynamic environment, are almost infinite. An alternative approach is then to skip the diagnosis and let the robot discover by trial and error a behavior that works in spite of the damage with a reinforcement learning algorithm 57, 48. However, current reinforcement learning algorithms require hundreds of trials/episodes to learn a single, often simplified, task 48, which makes them impossible to use for real robots and more ambitious tasks. We therefore need to design new trial-and-error algorithms that will allow robots to learn with a much smaller number of trials (typically, a dozen). We think the key idea is to guide online learning on the physical robot with dynamic simulations. For instance, in our recent work, we successfully mixed evolutionary search in simulation, physical tests on the robot, and machine learning to allow a robot to recover from physical damage 49, 1.

A final approach to address fragility is to deploy several robots or a swarm of robots or to make robots evolve in an active environment. We will consider several paradigms such as (1) those inspired from collective natural phenomena in which the environment plays an active role for coordinating the activity of a huge number of biological entities such as ants and (2) those based on online learning 45. We envision to transfer our knowledge of such phenomenon to engineer new artificial devices such as an intelligent floor (which is in fact a spatially distributed network in which each node can sense, compute and communicate with contiguous nodes and can interact with moving entities on top of it) in order to assist people and robots (see the principle in 55, 45, 40).

Scientific context

Interaction with the environment is a primordial requirement for an autonomous robot. When the environment is sensorized, the interaction can include localizing, tracking, and recognizing the behavior of robots and humans. One specific issue lies in the lack of predictive models for human behavior and a critical constraint arises from the incomplete knowledge of the environment and the other agents.

On the other hand, when working in the proximity of or directly with humans, robots must be capable of safely interacting with them, which calls upon a mixture of physical and social skills. Currently, robot operators are usually trained and specialized but potential end-users of robots for service or personal assistance are not skilled robotics experts, which means that the robot needs to be accepted as reliable, trustworthy and efficient 61. Most Human-Robot Interaction (HRI) studies focus on verbal communication 56 but applications such as assistance robotics require a deeper knowledge of the intertwined exchange of social and physical signals to provide suitable robot controllers.

Main challenges

We are here interested in building the bricks for a situated HRI addressing both the physical and social dimension of the close interaction, and the cognitive aspects related to the analysis and interpretation of human movement and activity.

The combination of physical and social signals into robot control is a crucial investigation for assistance robots 58 and robotic co-workers 54. A major obstacle is the control of physical interaction (precisely, the control of contact forces) between the robot and the human while both partners are moving. In mobile robots, this problem is usually addressed by planning the robot movement taking into account the human as an obstacle or as a target, then delegating the execution of this “high-level” motion to whole-body controllers, where a mixture of weighted tasks is used to account for the robot balance, constraints, and desired end-effector trajectories 39.

The first challenge is to make these controllers easier to deploy in real robotics systems, as currently they require a lot of tuning and can become very complex to handle the interaction with unknown dynamical systems such as humans. Here, the key is to combine machine learning techniques with such controllers.

The second challenge is to make the robot react and adapt online to the human feedback, exploiting the whole set of measurable verbal and non-verbal signals that humans naturally produce during a physical or social interaction. Technically, this means finding the optimal policy that adapts the robot controllers online, taking into account feedback from the human. Here, we need to carefully identify the significant feedback signals or some metrics of human feedback. In real-world conditions (i.e., outside the research laboratory environment) the set of signals is technologically limited by the robot's and environmental sensors and the onboard processing capabilities.

The third challenge is for a robot to be able to identify and track people on board. The motivation is to be able to estimate online either the position, the posture, or even moods and intentions of persons surrounding the robot. The main challenge is to be able to do that online, in real-time and in cluttered environments.

Angle of attack

Our key idea is to exploit the physical and social signals produced by the human during the interaction with the robot and the environment in controlled conditions, to learn simple models of human behavior and consequently to use these models to optimize the robot movements and actions. In a first phase, we will exploit human physical signals (e.g., posture and force measurements) to identify the elementary posture tasks during balance and physical interaction. The identified model will be used to optimize the robot whole-body control as prior knowledge to improve both the robot balance and the control of the interaction forces. Technically, we will combine weighted and prioritized controllers with stochastic optimization techniques. To adapt online the control of physical interaction and make it possible with human partners that are not robotics experts, we will exploit verbal and non-verbal signals (e.g., gaze, touch, prosody). The idea here is to estimate online from these signals the human intent along with some inter-individual factors that the robot can exploit to adapt its behavior, maximizing the engagement and acceptability during the interaction.

Another promising approach already investigated in the Larsen team is the capability for a robot and/or an intelligent space to localize humans in its surrounding environment and to understand their activities. This is an important issue to handle both for safe and efficient human-robot interaction.

Simultaneous Tracking and Activity Recognition (STAR) 60 is an approach we want to develop. The activity of a person is highly correlated with his position, and this approach aims at combining tracking and activity recognition to make one benefit from the other. By tracking the individual, the system may help infer its possible activity, while by estimating the activity of the individual, the system may make a better prediction of his/her possible future positions (especially in the case of occlusions). This direction has been tested with simulator and particle filters 44, and one promising direction would be to couple STAR with decision making formalisms like partially observable Markov decision processes (POMDPs). This would allow us to formalize problems such as deciding which action to take given an estimate of the human location and activity. This could also formalize other problems linked to the active sensing direction of the team: how should the robotic system choose its actions in order to better estimate the human location and activity (for instance by moving in the environment or by changing the orientation of its cameras)?

Another issue we want to address is robotic human body pose estimation. Human body pose estimation consists of tracking body parts by analyzing a sequence of input images from single or multiple cameras.

Human posture analysis is of high value for human robot interaction and activity recognition. However, even though the arrival of new sensors like RGB-D cameras has simplified the problem, it still poses a great challenge, especially if we want to do it online, on a robot and in realistic world conditions (cluttered environment). This is even more difficult for a robot to bring together different capabilities both at the perception and navigation level 43. This will be tackled through different techniques, going from Bayesian state estimation (particle filtering), to learning, active and distributed sensing.

7.1.1 inria_wbc

• Name:
  Inria whole body controller
• Keyword:
  Robotics
• Scientific Description:
  This software implements Task-Space Inverse Dynamics for the Talos robot, iCub Robot, Franka-Emika Panda robot, and Tiago Robot.
• Functional Description:
  This controller exploits the TSID library (QP-based inverse dynamics) to implement a position controller for the Talos humanoid robot. It includes:

  • flexible configuration files,
  • links with the RobotDART library for easy simulations,
  • stabilizer and torque safety checks.
• Release Contributions:
  First version
• URL:
  https://­github.­com/­resibots/­inria_wbc
• Publication:
  hal-03245005
• Contact:
  Jean-Baptiste Mouret
• Participants:
  Jean-Baptiste Mouret, Eloise Dalin, Ivan Bergonzani, Olivier Rochel

7.1.2 libProMP

• Name:
  Probabilistic Motion Primitives
• Keywords:
  Machine learning, Robotics
• Functional Description:
  This library implements Probabilistic Motion Primitives for motion prediction, motion recognition, and learning from demonstration in robotics. It leverages the Eigen3 library.
• URL:
  https://­github.­com/­hucebot/­promp
• Publication:
  hal-03281827v3
• Contact:
  Serena Ivaldi
• Participants:
  Luigi Penco, Ivan Bergonzani, Waldez Azevedo Gomes Junior, Serena Ivaldi, Jean-Baptiste Mouret

8.1.1 Planning and decision making

8.1.2 Learning and optimization

8.2.1 Teleoperation and Human-Robot collaboration

8.2.2 Human understanding

8.2.3 Exoskeleton and ergonomics

Two PhD grants (Cifre) with Stellantis

Participants: François Charpillet, Julien Uzzan, Lina Achaji.

Stellantis and Inria announced on July 5th, 2018 the creation of an OpenLab dedicated to artificial intelligence. The studied areas include autonomous and intelligent vehicles, mobility services, manufacturing, design development tools, the design itself and digital marketing as well as quality and finance. Two PhD programs have been launched with the Larsen team in this context : One with Lina Achaji about pedestrian trajectory prediction, one with Julien Uzzan about Reinforcement Learning. Julien had defended his PhD in 2022 and Lina defended on 2023-07-05. Both theses are under embargo for publication.

PhD grant with SAFRAN

Participants: François Charpillet, Pauline Maurice, Serena Ivaldi, Alexandre Oliveira Souza.

Collaboration with Jordane Grenier (Safran) and Christophe Guettier (Safran).

The thesis is funded by Safran to develop the AI-based control of their hybrid exoskeleton, based on the one developed in the DGA-Rapid porject ASMOA. It consists in developing methods to predict the amount of assistance that is needed by the human in tasks invovling payload manipulation.

PhD work co-advised with CEA-LIST

Participants: Jacques Zhong, Francis Colas, Pauline Maurice.

Collaboration with Vincent Weistroffer (CEA-LIST) and Claude Andriot (CEA-LIST)

This PhD work started in October, 2020. The objective is to develop a software tool that allows taking into account the diversity of workers' morphology when designing an industrial workstation. The developed tool will enable us to test the feasibility and ergonomics of a task for any morphology of workers, based on a few demonstrations of the task in virtual reality by one single worker. The two underlying scientific questions are i) the automatic identification of the task features from a few virtual reality (VR) demonstrations, and ii) the transfer of the identified task to digital human models of various morphologies.

10.1.1 Visits to international teams

10.2.1 Horizon Europe

10.3.1 ANR : The Flying Co-Worker

Participants: François Charpillet, Olivier Buffet, Francis Colas, Serena Ivaldi, Vincent Thomas.

• Program:
  ANR
• Project title:
  Flying Co-Worker
• Duration:
  October 2019 – October 2023
• Coordinator:
  Daniel Sidobre (LAAS-CNRS, Toulouse)
• Local coordinator:
  François Charpillet
• Abstract:
  Bringing together recent progress in physical and decisional interaction between humans and robots with the control of aerial manipulators, this project addresses the flying coworker, an aerial manipulator robot that acts as a teammate of a human worker to transport a long bar or to realise complex tasks. Safety and human-aware robot abilities are at the core of the proposed research to progressively build robots capable to do cooperative handling and to assist a worker by notably delivering objects directly in a safe, efficient, pertinent and acceptable manner. The methodologies developed for ground manipulators cannot be directly used for aerial manipulator systems because of the floating base, of a limited payload, and of strong actuation and energy constraints. From the perception and the interpretation of the human activity, the objective of the project is to build an aerial manipulator capable to plan and control human aware motions to achieve collaborative tasks.

10.3.2 ANR : PLASMA

Participant: Olivier Buffet.

• Program:
  ANR
• Project acronym:
  PLASMA
• Project title:
  Planification et Apprentissage pour Agir dans des Systèmes Multi-Agents
• Duration:
  February 2020 – October 2023
• Coordinator:
  Jilles Dibangoye (INSA-Lyon)
• Local coordinator:
  Olivier Buffet
• Abstract:
  The main research goal is to develop a general theory and algorithms with provable guarantees to treat planning and (deep) Reinforcement Learning problems arising from the study of multi-agent sequential decision-making, which may be described as Partially Observable Stochastic Games (POSGs).

10.3.3 ANR : Proxilearn

Participant: Jean-Baptiste Mouret.

• Program:
  ANR-DGA ASTRID
• Project acronym:
  Proxilearn
• Project title:
  Learning for Proximity Flying
• Duration:
  January 2020 – December 2023
• Coordinator:
  Jean-Baptiste Mouret
• Partner:
  • Institut des sciences du Mouvement, CNRS/Aix-Marseille Université
• Summary:
  The Proxilearn project leverages artificial intelligence techniques to make it possible for a micro-UAV (10-20 cm / 50-80 g) to fly in very confined spaces (diameter between 40 cm and 1.5 m): air ducts, tunnels, natural caves, quarries, ...It is focused on two challenges: (1) stabilizing a UAV in spite of the turbulences created by the interaction between the rotors and the environment, and (2) the flight in autonomy with very little light.

10.3.4 ANR: ROOIBOS

Participants: Pauline Maurice, Francis Colas, Vincent Thomas.

• Program:
  ANR JCJC
• Project acronym:
  ROOIBOS
• Project title:
  User-Specific Adaptation of Collaborative Robot Motion for Improved Ergonomics
• Duration:
  March 2021 – February 2025
• Coordinator:
  Pauline Maurice (CNRS)
• Summary:
  Collaborative robots have the potential to reduce work-related musculoskeletal disorders not only by decreasing the workers' physical load, but also by modifying and improving their postures. Imposing a sudden modification of one's movement can however be detrimental to the acceptance and efficacy of the human-robot collaboration. In ROOIBOS, we will develop a framework to plan user-specific trajectories for collaborative robots, to gradually optimize the efficacy of the collaboration and the long-term occupational health of the user. We will use machine learning and probabilistic methods to perform user-specific prediction of whole-body movements. We will define dedicated metrics to evaluate the movement ergonomic performance and intuitiveness. We will integrate those elements in a digital human simulation to plan a progressive adaptation of the robot motion accounting for the user's motor preferences. We will then use probabilistic decision-making to adapt the plan on-line to the user's motor adaptation capabilities. This will enable a smooth deployment of collaborative robots at work.

10.3.5 ANR : EpiRL

Participants: Olivier Buffet.

• Program:
  ANR
• Project title:
  Apprentissage par renforcement épistémique
• Duration:
  February 2023 – February 2027
• Coordinator:
  François Schwarzentruber (IRISA, École normale supérieure de Rennes)
• Abstract:

  EpiRL project aims at investigating the combination of epistemic planning and reinforcement learning (RL), by proposing new algorithms that are efficient, adaptive, and capable of computing decisions relying on theory of knowledge and belief. We expect from this approach an efficiency in the generation of epistemic plans, while decisions made RL algorithms will be explainable. Moreover, the algorithms of EpiRL will be tested and evaluated within a real application that exploits autonomous agents.

  The project will address the weaknesses of both epistemic planning and RL: on the one hand, existing epistemic planning algorithms are costly, do not adapt to the environment, and concepts are hand-crafted and are not learned; on the other hand, in reinforcement learning, agents adapt to their environments but are unable to reason about beliefs of other agents. The newly developed algorithms will combine the strengths of both fields.

  We propose four workpackages:

  1. Study representations of states
  2. Develop RL algorithms
  3. Study representations of policies
  4. Validating our algorithms with our industrial partner DAVI. In particular, we aim at developing a debunking chatbot whose use case will apply to raising awareness about environmental issues.

11.1.1 Scientific events: organisation

11.1.2 Scientific events: selection

11.1.3 Journal

11.1.4 Invited talks

• Serena Ivaldi was invited as keynote speaker at IEEE IROS 2023; then she gave invited talks at 3 IEEE IROS workshops. She was also invited as speaker at Wearacon Europe 2023.

11.1.5 Leadership within the scientific community

• Serena Ivaldi was co-chair of the IEEE/RAS ICRA Steering Committee; Associate Vice-President of IEEE/RAS Member Activities Boards (MAB); member of the IEEE/RAS Education Committee; co-leader of the Working Group on Humanoid Robots (GT7 Humanoïde) of the French Robotics Society (GDR Robotique) until summer 2023.

11.1.6 Scientific expertise

• Olivier Buffet reviewed a project for ANR.
• Pauline Maurice was member of an ANR reviewing committee
• Serena Ivaldi was reviewer of the European Project FELICE (2nd project evaluation); she is in the advisory board of two European projects (Agimus and PILLAR-Robots) ; she reviewed two ERC projects submissions (ERC ADV and ERC Synergy)

11.1.7 Research administration

• Amine Boumaza and Francis Colas are members of the comité de centre of Inria Centre at Université de Lorraine.
• Francis Colas was member of the Evaluation Commission of Inria (until Aug. 2023).
• Francis Colas was member of the hiring committee for junior research scientists at Inria Centre at Rennes University.
• Francis Colas was member of the hiring committee for junior research scientists at Inria Centre at the University Grenoble Alpes.
• Francis Colas is member of the Comipers committee of Inria Centre at Université de Lorraine.
• Serena Ivaldi is member of Bureau du Comité des Projets (BCP) of Inria Centre at Université de Lorraine.
• Enrico Mingo Hoffman is corresponding chair of the IEEE-RAS Technical Committee on Whole-Body Control.
• Jean-Baptiste Mouret is head of the Department 5 of the LORIA (UMR 7503).

11.2.1 Teaching

• Master: Vincent Thomas is co-responsible for the parcours “Apprentissage, Vision, Robotique” of the Computer Science Master, Univ. Lorraine and co-conducted the evolution of this parcours for the new training offer of Univ. Lorraine.

• Master: Amine Boumaza, “Méta-heuristiques et recherche locale stochastique”, 30h eq. TD, M1 informatique, Univ. Lorraine, France.
• Master: Amine Boumaza, “Modélisation de Phénomènes Biologiques ”, 12h eq. TD, M1 Sciences Cognitives, Univ. Lorraine, France.
• Tutorial: Olivier Buffet, “Planification dans l'incertain", 2h CM, CNRS Formation Entreprise.
• Master: Francis Colas, “Planification de trajectoires”, 15h eq. TD, M2 “Apprentissage, Vision, Robotique”, Univ. Lorraine, France.
• Master: Francis Colas, “ROS”, 6h eq. TD, M2 “Apprentissage, Vision, Robotique”, Univ. Lorraine, France.
• Master: Francis Colas, “Intégration méthodologique”, 36h eq. TD, M2 “Apprentissage, Vision, Robotique”, Univ. Lorraine, France.
• Master: Serena Ivaldi, “Analyse Comportementale”, 16h CM/TD, M2 “Sciences Cognitives”, Univ. Lorraine, France.
• Master: Pauline Maurice, “Analyse Comportementale”, 12h CM/TP, M2 “Sciences Cognitives”, Univ. Lorraine, France.
• Master: Jean-Baptiste Mouret, “Quality Diversity”, 3h (M2 Innovation, Mines Paristech)
• Tutorial, Jean-Baptiste Mouret, “Quality Diversity Optimization”, 3h, ACM GECCO 2020 (with A. Cully, Imperial College, and S. Doncieux, Sorbonne Université)
• Master: Alexis Scheuer, “Introduction à la robotique autonome”, 30h eq. TD, M1 informatique, Univ. Lorraine, France.
• Master: Alexis Scheuer, “Modélisation et commande en robotique”, 16h eq. TD, M2 “Apprentissage, Vision, Robotique”, Univ. Lorraine, France.
• Master: Alexis Scheuer, “Éléments de robotique”, 4h eq. TD, Master MEEF 2d degré, INSPÉ, Univ. Lorraine, France.
• Master: Vincent Thomas, “Apprentissage et raisonnement dans l'incertain”, 15h eq. TD, M2 M2 “Apprentissage, Vision, Robotique”, Univ. Lorraine, France.
• Master: Vincent Thomas, “Game Design”, 30h eq. TD, M1 Sciences Cognitives, Univ. Lorraine, France.
• Master: Vincent Thomas, “Agent intelligent”, 15h eq. TD, M1 Sciences Cognitives, Univ. Lorraine, France.
• Tutorial: Olivier Buffet, “Planification dans l'incertain", 2h CM, CNRS Formation Entreprise.

11.2.2 Supervision

• PhD: Yoann Fleytoux, “Human-guided manipulation learning of irregular objects”, 2023-12-01, Jean-Baptiste Mouret (advisor), Serena Ivaldi (co-advisor) 28
• PhD: Yang You, “Probabilistic Decision-Making Models for Multi-Agent Systems and Human-Robot Collaboration”, 2023-02-22, Olivier Buffet (advisor), Vincent Thomas (co-advisor) 29
• PhD: Lina Achaji, Cifre with PSA, “Modélisation de systèmes dynamiques par des réseaux de neurones à mémoire courte et longue : application pour la prédiction de l'état d'environnement routier”, 2023-07-05, François Charpillet (advisor), François Aioun (Co-advisor, PSA), confidential.
• PhD in progress: Nima Mehdi, “Perception et interprétation de l'activité humaine”, started in May 2020, Francis Colas (advisor).
• PhD in progress: Timothée Anne “Meta-learning for adaptive whole-body control”, started in September 2020, Jean-Baptiste Mouret (advisor)
• PhD in progress: Jacques Zhong, “Prise en compte de la variabilité de morphologie de l'opérateur dans des tâches de montage simulées en réalité virtuelle”, started in October 2020, Francis Colas (advisor), Pauline Maurice (co-advisor), Vincent Weistroffer (co-advisor, CEA-LIST).
• PhD in progress: Abir Bouaouda, “Apprentissage automatique pour le contrôle des systèmes complexes. Application aux robots à câbles”, started in October 2020, Mohamed Boutayeb (advisor, CRAN), Dominique Martinez (co-advisor)
• PhD in progress: Raphaël Bousigues, “Collaborative robots as a tool for optimizing skill acquisition through the appropriate use of human motor variability”, started in December 2020, Pauline Maurice (co-advisor), Vincent Padois (advisor, INRIA Bordeaux), David Daney (co-advisor, INRIA Bordeaux).
• PhD in progress: Aya Yaacoub, “Planification individu-spécifique du comportement d’un robot collaboratif pour la prévention des troubles musculo-squelettiques”, started in December 2021, Francis Colas (advisor), Pauline Maurice (co-advisor).
• PhD in progress: Alexandre Oliveira Souza, “Intelligence Artificielle et contrôle de systèmes interactifs : application aux exosquelettes”, started in October 2022, François Charpillet (advisor), Pauline Maurice (co-advisor).
• PhD in progress: Salomé Lepers, “Explicabilité et interprétabilité en planification probabiliste”, started in October 2022, Olivier Buffet (advisor), Vincent Thomas (co-advisor).
• PhD in progress: Dionis Totsila, EuROBIN project, “Learning Bimanual robot skills from human demonstrations and natural language ”, started in November 2023, Serena Ivaldi (advisor) and Jean-Baptiste Mouret (co-advisor)

11.2.3 Juries

• Olivier Buffet was
  • reviewer for the PhD of Yassine El Manyari (Université de Nantes)
• Francis Colas was
  • examiner for the PhD of Jérôme Truc (Université de Toulouse)
• Vincent Thomas was
  • examiner for the PhD of Giorgio Angelotti (Université de Toulouse Midi-Pyrénées)
  • examiner for the PhD of Junkang Li (Université de Caen Normandie)
• Jean-Baptiste Mouret was:
  • reviewer for the PhD of Michel Aractingi (CNRS-LAAS & INSA Toulouse)
• Serena Ivaldi was:
  • reviewer for the PhD of Angela Mazzeo (Scuola Superiore Sant'Anna, Italy), Trifun Savic (Inria Paris & Sorbonne Université)
  • examiner for the PhD of Samuel Beaussant (Université Clermont Auvergne), Nuno Duarte (IST University of Lisbon, Portugal)

11.3.1 Internal or external Inria responsibilities

• Amine Boumaza is a member of the editorial board of Interstices

11.3.2 Articles and contents

• Francis Colas, Serena Ivaldi and Jean-Baptiste Mouret were interviewed for l'Est Républicain
• Jean-Baptiste Mouret was interviewed by France Info (France Info Junior)
• Jean-Baptiste Mouret was interviewed by the German TV Channel Saarländischer Rundfunk
• Jean-Baptiste Mouret was hosted by the Arte TV show “Scope” [2.5 hours]
• Pauline Maurice gave an interview for the podcast "Plus tard je serai"
• Serena Ivaldi was interviewed by el Diario (Spain)

11.3.3 Education

• Vincent Thomas
  • proposed one workshop for future teachers during journée SNT-NSI 2023: “reinforcement learning”;
  • participated as a jury member of the scientific Game Jam organized by the “Ecole des Mines de Nancy” and Jean Lamour Institute;
  • participated at the “fête de la science” by co-animating an introductory robotics workshop for pupils.
• Olivier Buffet participated at the “fête de la science” by co-animating an introductory robotics workshop for pupils.
• Alexis Scheuer participated at the “fête de la science” by organizing and co-animating an introductory robotics workshop for pupils.

International journals

• 3 articleK.Kourosh Darvish, L.Luigi Penco, J.Joao Ramos, R.Rafael Cisneros, J.Jerry Pratt, E.Eiichi Yoshida, S.Serena Ivaldi and D.Daniele Pucci. Teleoperation of Humanoid Robots: A Survey.IEEE Transactions on Robotics2023HALDOIback to text
• 4 articleA.Aurélien Delage, O.Olivier Buffet, J.Jilles Dibangoye and A.Abdallah Saffidine. HSVI Can Solve Zero-Sum Partially Observable Stochastic Games.Dynamic Games and ApplicationsSeptember 2023, 58HALDOIback to text
• 5 articleA.Aya Yaacoub, V.Vincent Thomas, F.Francis Colas and P.Pauline Maurice. A Probabilistic Model for Cobot Decision Making to Mitigate Human Fatigue in Repetitive Co-manipulation Tasks.IEEE Robotics and Automation LettersSeptember 2023HALDOIback to text
• 6 articleJ.Jacques Zhong, V.Vincent Weistroffer, J.-B.Jean-Baptiste Mouret, F.Francis Colas and P.Pauline Maurice. Workstation Suitability Maps: Generating Ergonomic Behaviors on a Population of Virtual Humans with Multi-task Optimization.IEEE Robotics and Automation LettersSeptember 2023, 1-8HALDOIback to text

International peer-reviewed conferences

• 7 inproceedingsT.Timothee Anne and J.-B.Jean-Baptiste Mouret. Multi-Task Multi-Behavior MAP-Elites.GECCO '23 Companion: Proceedings of the Companion Conference on Genetic and Evolutionary ComputationGECCO '23 Companion: Proceedings of the Companion Conference on Genetic and Evolutionary ComputationLisbon, Portugal, France2023HALDOIback to text
• 8 inproceedingsT.Thais Bernardi, Y.Yoann Fleytoux, J.-B.Jean-Baptiste Mouret and S.Serena Ivaldi. Learning height for top-down grasps with the DIGIT sensor.Proc. IEEE RAS Int. Conf. Robotics and Automation (ICRA)IEEE RAS Int. Conf. Robotics and Automation (ICRA)London, United Kingdom2023HALback to text
• 9 inproceedingsA.Abir Bouaouda, R.Rémi Pannequin, F.François Charpillet, D.Dominique Martinez and M.Mohamed Boutayeb. Dynamic modeling and AI-based control of a cable-driven parallel robot.22nd IFAC World Congress, IFAC 2023Yokohama, JapanJuly 2023HALDOIback to text
• 10 inproceedingsJ.Jessica Colombel, D.David Daney and F.François Charpillet. Holistic view of Inverse Optimal Control by introducing projections on singularity curves.IEEE International Conference on Robotics and Automation (ICRA)2023 IEEE International Conference on Robotics and Automation (ICRA)London, United KingdomIEEEMay 2023, 12240-12246HALDOIback to text
• 11 inproceedingsA.Aurélien Delage, O.Olivier Buffet and J.Jilles Dibangoye. Global min-max Computation for α-Hölder Games.Proceedings of the 35th IEEE International Conference on Tools with Artificial Intelligence (ICTAI)ICTAI 2023 - 35th IEEE International Conference on Tools with Artificial IntelligenceAtlanta (Georgia), United StatesIEEE Computer SocietyNovember 2023, 518-525HALback to textback to text
• 12 inproceedingsM.Mahdiar Edraki, P.Pauline Maurice and D.Dagmar Sternad. Humans Need Augmented Feedback to Physically Track Non-Biological Robot Movements.2023 IEEE International Conference on Robotics and Automation (ICRA)IEEE International Conference on Robotics and Automation (ICRA 2023)Londres, United KingdomIEEE2023HALDOIback to text
• 13 inproceedingsS.Serena Ivaldi and E.Edoardo Ghini. Teleoperating a robot for removing asbestos tiles on roofs: insights from a pilot study.IEEE ARSO - 2023 IEEE International Conference on Advanced Robotics and Its Social ImpactsBerlin (Germany), GermanyJune 2023HALDOI
• 14 inproceedingsS.Sophie Lemonnier, L.Laura Cavagnac, N.Nathan Kohili, K.Kévin Bouillet, P.Pauline Maurice and G.Guillaume Mornieux. Sapeurs-pompiers et désincarcération : effet du port d'un exosquelette sur la charge cognitive et l'acceptation.EPIQUE 2023 - 12ème colloque de Psychologie ErgonomiqueParis, France2023HALback to text
• 15 inproceedingsS.Salomé Lepers, V.Vincent Thomas and O.Olivier Buffet. Comment rendre des comportements plus prédictibles.JIAF-JFPDA - Journées d’Intelligence Artificielle FondamentaleStrasbourg, FranceJuly 2023HALback to text
• 16 inproceedingsP.P Maurice, S.S Lemonnier, N.N Kohili, L.L Cavagnac and G.G Mornieux. Biomechanical effects of using a passive upper-limb exoskeleton to assist firefighters during vehicle extrication maneuver.48ème congrès de la Société de BiomécaniqueGrenoble, FranceOctober 2023HALback to text
• 17 inproceedingsA.Alexandre Oliveira Souza, J.Jordane Grenier, F.François Charpillet, P.Pauline Maurice and S.Serena Ivaldi. Towards data-driven predictive control of active upper-body exoskeletons for load carrying.2023 IEEE International Conference on Advanced Robotics and Its Social Impacts (ARSO)International Conference on Advanced Robotics and its Social ImpactsBerlin, Germany2023HALDOIback to textback to text
• 18 inproceedingsL.Luigi Penco, J.-B.Jean-Baptiste Mouret and S.Serena Ivaldi. Prescient Teleoperation of Humanoid Robots.2023 IEEE RAS International Conference on Humanoid Robots2023 IEEE RAS International Conference on Humanoid RobotsAustin, United StatesIEEEDecember 2023HALDOIback to text
• 19 inproceedingsQ.Quentin Rouxel, R.Ruoshi Wen, Z.Zhibin Li, C.Carlo Tiseo, J.-B.Jean-Baptiste Mouret and S.Serena Ivaldi. Feasibility Retargeting for Multi-contact Teleoperation and Physical Interaction.2nd Workshop Toward Robot Avatars, 2023 IEEE International Conference on Robotics and Automation (ICRA)London, United KingdomJune 2023HALDOI
• 20 inproceedingsY.Yang You, V.Vincent Thomas, F.Francis Colas, R.Rachid Alami and O.Olivier Buffet. Robust Robot Planning for Human-Robot Collaboration.2023 IEEE International Conference on Robotics and Automation (ICRA)London, United KingdomIEEEMay 2023, 9793-9799HALDOIback to text
• 21 inproceedingsY.Yang You, V.Vincent Thomas, F.Francis Colas and O.Olivier Buffet. Monte-Carlo Search for an Equilibrium in Dec-POMDPs.The 39th Conference on Uncertainty in Artificial Intelligence (UAI)Pittsburgh, PA, United StatesMay 2023HALDOIback to text

Conferences without proceedings

• 22 inproceedingsL.Lina Achaji, J.Julien Moreau, F.François Aioun and F.François Charpillet. Analysis over vision-based models for pedestrian action anticipation.3rd Workshop on the Prediction of Pedestrian Behaviors for Automated Driving @ 26th IEEE International Conference on Intelligent Transportation Systems ITSC 2023Bilbao, SpainMay 2023, 6HALback to text
• 23 inproceedingsA.Aurélien Delage, O.Olivier Buffet and J.Jilles Dibangoye. Global Min-Max Computation for α-Hölder Zero-Sum Games.GAIW 2023 - 5th Games, Agents, and Incentives WorkshopLondres (London), United Kingdom2023, 1-9HALback to text
• 24 inproceedingsA.Aurélien Delage, O.Olivier Buffet, J.Jilles Dibangoye and A.Abdallah Saffidine. Heuristic Search Value Iteration can solve zero-sum Partially Observable Stochastic Games.MSDM 202311th Multiagent Sequential Decision Making under Uncertainty Workshop ; Held as part of the Workshops at the IFAAMAS 2023 - 21st International Conference on Autonomous Agents and Multiagent SystemsLondres, United KingdomMay 2023HALback to text
• 25 inproceedingsJ.-B.Jean-Baptiste Mouret. Fast generation of centroids for MAP-Elites.GECCO '23 Companion: Companion Conference on Genetic and Evolutionary ComputationLisbon, PortugalACMJuly 2023, 155-158HALDOI
• 26 inproceedingsA.Aya Yaacoub, V.Vincent Thomas, F.Francis Colas and P.Pauline Maurice. Fatigue Mitigation with Cobots: Investigating the Cobot Workspace.Journée des Jeunes Chercheurs en RobotiqueMoliets et Maa, FranceOctober 2023HALback to text

Edition (books, proceedings, special issue of a journal)

• 27 periodicalMaison Intelligente.Revue Ouverte d'Intelligence Artificielle4https://roia.centre-mersenne.org/volume/ROIA_2023__4_1/1May 2023, 1-156HAL

Doctoral dissertations and habilitation theses

• 28 thesisY.Yoann Fleytoux. Learning human preferences for robotic grasping.Université de LorraineDecember 2023HALback to text
• 29 thesisY.Yang You. Probabilistic Decision-Making Models for Multi-Agent Systems and Human-Robot Collaboration.Université de LorraineFebruary 2023HALback to textback to text

Reports & preprints

• 30 miscM.Mihai Andries, Y.Yoann Fleytoux, S.Serena Ivaldi and J.-B.J.-B. Mouret. AGOD-Grasp: an Automatically Generated Object Dataset for benchmarking and training robotic grasping algorithms.March 2023HAL
• 31 miscN.Nima Mehdi, V.Vincent Thomas, S.Serena Ivaldi and F.Francis Colas. Duration Models for Human Activity Prediction.2023HALback to text
• 32 miscL.Luigi Penco, J.-B.Jean-Baptiste Mouret and S.Serena Ivaldi. Prescient teleoperation of humanoid robots - Supplementary material.2023HALDOI
• 33 miscF.Federico Rollo, A.Andrea Zunino, N.Nikolaos Tsagarakis, E.Enrico Mingo Hoffman and A.Arash Ajoudani. CARPE-ID: Continuously Adaptable Re-identification for Personalized Robot Assistance.November 2023HAL
• 34 miscQ.Quentin Rouxel, S.Serena Ivaldi and J.-B.Jean-Baptiste Mouret. Multi-Contact Whole Body Force Control for Position-Controlled Robots.December 2023HAL

Other scientific publications

• 35 inproceedingsA.Amine Boumaza. Promoting Originality in Online Swarm Robotics.GECCO '23Lisbon, PortugalACM; ACM; ACMJuly 2023, 115-118HALDOIback to text
• 36 inproceedingsM.Mahdiar Edraki, P.Pauline Maurice and D.Dagmar Sternad. Robot Motion Affects Human Force Regulation in Physical Human-Robot Interaction.IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2023) - Workshop on Multilimb Coordination in Human Neuroscience and Robotics: Classical and Learning PerspectivesDetroit, United StatesOctober 2023HALback to text
• 37 miscQ.Quentin Rouxel, S.Serena Ivaldi and J.-B.Jean-Baptiste Mouret. Multi-Contact Whole Body Force Control for Position-Controlled Robots.October 2023HAL
• 38 inproceedingsJ.Jacques Zhong, P.Pauline Maurice, V.Vincent Weistroffer and F.Francis Colas. Multi-task Optimization to Evaluate Workstation Suitability over a Population of Virtual Humans.JNRH 2023 - Journées Nationales de la Robotique HumanoïdeBordeaux, FranceJuly 2023HALback to text