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 47, 57. 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 47, 52.

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 51.

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 with no attempt at distinguishing the suitable changes. For localization in not-too-cluttered or not-too-empty environments, this is generally sufficient as a significant fraction of the environment should remain stable. But for life-long autonomy, and in particular 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 46 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 62, 52. However, current reinforcement learning algorithms require hundreds of trials/episodes to learn a single, often simplified, task 52, 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 53, 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 50. 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 60, 50, 45).

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 67. Most Human-Robot Interaction (HRI) studies focus on verbal communication 61 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 Human-Robot Interaction (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 63 and robotic co-workers 59. 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 44.

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) 66 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 49, 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 48. This will be tackled through different techniques, going from Bayesian state estimation (particle filtering), to learning, active and distributed sensing.

7.1.1 RobotDART

• Name:
  RobotDART
• Keywords:
  Physical simulation, Robotics, Robot Panda
• Functional Description:
  RobotDart combines:

  • high-level functions around DART to simulate robots,
  • a 3D engine based on the Magnum library (for visualisation),
  • a library of common research robots (Franka, IIWA, TALOS, iCub),
  • abstractions for sensors (including cameras).
• News of the Year:
   

  • new URDF library (Utheque)
  • support for Tiago and UR3
  • numerous bugfixes and API improvements
• URL:
  https://­github.­com/­resibots/­robot_dart
• Contact:
  Jean-Baptiste Mouret
• Partner:
  University of Patras

7.1.2 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
• News of the Year:
   

  • new stabilization methods
  • numerous bug fixes
  • support of iCub and Tiago
• URL:
  https://­github.­com/­resibots/­inria_wbc
• Publication:
  hal-03245005
• Contact:
  Jean-Baptiste Mouret
• Participants:
  Jean-Baptiste Mouret, Eloise Dalin, Ivan Bergonzani

8.1.1 Planning and decision making

8.1.2 Multirobot and swarm

8.2.1 Teleoperation and Human-Robot collaboration

Whole-body teleoperation

Participants: Jean-Baptiste Mouret, Serena Ivaldi, Eloïse Dalin, Ivan Bergonzani, Luigi Penco, Evelyn D'Elia.

See caption for description.

Thanks to the ADT wbTorque and the DGA grant “Humanoïde résilient”, we now have a reliable controller for task-based inverse dynamics. This controller takes reference positions for the hands, feet and center of mass and computes the optimal joint torques (from which positions can be computed) for each of the 32 joints. The main achievements of 2021 are:

• The Talos robot can now be tele-operated with the Xsens motion capture suit: video ;
• We improved the stabilization/balance;
• We ported the last version of this controller to the iCub humanoid robot (teleoperation with the Xsens suit) and the Tiago robot (arm + mobile base, teleoperated with an haptic 6-D joystick).

We documented our current implementation in a workshop paper: 20. The code is available online (github).

In the context of the AnDy project, there were two main activities:

• we kept developing the method for teleoperating humanoid robots in presence of delays in communication, using predictive models based on ProMPs. The results were described into an article 56 now under review.
• we investigated multi-objective optimisation strategies to automatically optimize the task weights and gains of the Talos' whole-body controller, to be more precise in tracking desired reference trajectories while being more robust in balancing. The preliminary results were presented in a short paper at a ICRA 2021 workshop 43; we refined our approach by combining learned Pareto-optimal solution with Bayesian optimization. A full article presenting our results was submitted to a conference.

8.2.2 Human understanding

8.2.3 Exoskeleton and ergonomics

PhD grant (Cifre) with Diatelic Pharmagest

Participants: François Charpillet, Yassine El Khadiri.

We have a long term collaboration with the Diatelic company, a start-up created by François Charpillet and colleagues in 2002. Currently we have a collaboration through a Cifre PhD whose objective is to work on daily activity recognition for monitoring elderly people at home. The work will be included in a product that will be launched next year (CareLib solution). The PhD Student, Yassine El Khadiri has defended his PhD thesis on August 31.

Two PhD grants (Cifre) with PSA

Participants: François Charpillet, Julien Uzzan, Lina Achaji.

Groupe PSA 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. A dozen topics is being covered over a 4-year period. Two PhD programs have been launched with the Larsen team in this context.

PhD grant with SAFRAN

Participants: François Charpillet, Nicolas Gauville, Christophe Guettier (Safran).

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 VR demonstrations, and ii) the transfer of the identified task to digital human models of various morphologies.

10.1.1 FP7 & H2020 projects

10.1.2 Other European programs/initiatives

10.2.1 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 act 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.

10.2.2 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).

10.2.3 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.

10.2.4 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.

10.2.5 ANR-PIA Pherosensor: Early detection of pest insects using pheromone receptor-based olfactory sensors

Participants: Dominique Martinez.

• Coordinator
  Philippe Lucas (Inrae Versailles)
• Duration
  February 2021 – February 2026
• Summary:
  Insects directly or indirectly destroy one third of the annual world's crop production. In a context of climate change and world trade increase, early detection of native and invasive insect pests is an urgent challenge for optimal action before infestation settles. Insect use species-specific pheromones to attract conspecifics from the same sex (sex pheromones, e.g. in moths) or from both sexes (aggregation pheromones, e.g. in weevils). The detection of insect pheromones is a promising alternative for insect surveillance, yet it is a challenge because of the low amounts emitted. PheroSensor goes beyond the current state-of-the-art solutions of artificial odor sensing by developing and evaluating innovative bioinspired sensors to detect insect pests. We develop sensors engineered from insect pheromone receptors coupled with artificial intelligence techniques to detect and monitor three installed or potentially invasive insect pests in France, the fall armyworm and cotton leafworm moths, and the red palm weevil.

10.2.6 Projet CNES: PHOeBUS

Participants: Dominique Martinez, François Charpillet, Abir Bouaouda.

• Project title:
  Flight Observation of Butterflies Under Space-like gravity
• Duration:
  June 2021 – December 2022
• Coordinator
  Mickaël Bourgoin (ENS Lyon)
• Summary:
  PHOeBUS, standing for “Flight ObsErvation of Butterflies Under Space-like gravity”, is a project that aims at studying flight adaptation of butterflies to micro and macro-gravity, during parabolic flights, as compared to free flights recorded using the cable-driven robot at Loria. The goals are both biological (how does the butterfly adapt to a radical environmental change) and aerodynamic (how does the flapping pattern and frequency evolve to adjust the lift force to new gravity conditions). The outcomes of the project are therefore of interest to a broad community, including biology, aerodynamics and bio-inspired dronautic technology (dimensionless flow numbers for the flight of Ingenuity drone on Mars are equivalent to those of butterflies on Earth).

10.2.7 Inria-DGA Resilient Humanoid

Participant: Jean-Baptiste Mouret.

• Program:
  Convention Inria-DGA about Artificial Intelligence
• Project title:
  Resilient humanoid
• Duration:
  October 2019 – October 2022
• Coordinator:
  Jean-Baptiste Mouret
• Summary:
  This project aims at designing adaptive controllers for humanoid robots: a tele-operated humanoid should be able to adapt to unforeseen conditions, including damage and obstacles. To do so, this project bridges the gap between task-space whole-body controllers based on quadratic programming and data-efficient reinforcement learning. [funding for 1 research engineer]

10.2.8 Inria-DGA U-Drone

Participant: Jean-Baptiste Mouret.

• Program:
  Convention Inria-DGA about Artificial Intelligence
• Project acronym:
  U-Drone
• Project title:
  Underground Drone
• Duration:
  October 2019 – October 2022
• Coordinator:
  Jean-Baptiste Mouret
• Summary:
  This project aims at making progress in the design of flying vehicles for highly confined spaces like pipes, caves, quarries, etc. It complements the Proxilearn ANR project. [funding for 1 research engineer]

10.2.9 DGA-RAPID ASMOA

Participants: François Charpillet, Serena Ivaldi, Pauline Maurice.

• Program:
  DGA-RAPID
• Project acronym:
  ASMOA
• Project title:
  Prédiction des intentions d'un utilisateur d'un exosquelette
• Duration:
  October 2019 – October 2022
• Coordinator:
  Kompaï Robotics
• Local coordinator:
  François Charpillet
• Partners:
  • Inria
  • Safran Electronics & Defense
• Summary:
  The ASMOA project is about intention prediction for exoskeleton control. The challenge is to detect the intention of movement of a user of exoskeletons for load handling, to optimize the exoskeleton control following the user’s intended movement, and to evaluate with an experimental campaign the effect of the intelligent controller on the user’s movement and performance. INRIA will develop the prediction and control method based on machine learning techniques for an upper-body active exoskeleton.

TELEMOVTOP

• Funding:
  AMI Numerique Grand Est
• Duration:
  2020/10 - 2022/02 (extended 2022/06 because of COVID-19)
• Coordinator:
  ISOTOP Etancheite
• Local coordinator:
  Serena Ivaldi
• Summary:
  The purpose of the project is to create a new intelligent and interactive robot to automatize the process of disposal of metal sheets on roofs contaminated by asbestos. The goal is to reduce the risks of contamination for workers exposed to asbestos, the risks of pollution, and at the same time introducing a novel robot in a market that needs to be innovated. The robot will have to be teleoperated by workers that are on remote sites; an interactive autonomy mode will leverage the robot's intelligent and autonomous behaviors while enabling the workers to supervise the job. Inria is reponsible for the high-level control, teleoperaton and HMI of the system.

11.1.1 Scientific events: organisation

11.1.2 Scientific events: selection

11.1.3 Journal

11.1.4 Invited talks

• Jean-Baptiste Mouret gave invited talks for the GT5-GT8 of GDR Robotique (learning and neuroscience for human-robot interaction), at the Human-Centered AI and robotics day in Montpellier (LIRMM), for the Réseau Régional de Recherche en Robotique (R4, Nouvelle Qquitaine), at the Journées Scientifiques Inria, at the “Healabe Soft Robots” workshop (Robosoft conference), and at the Neurips workshop on Robot Learning
• Pauline Maurice gave invited talks for the GT1-GT6 of GDR Robotique (workshop on occupational exoskeletons), at the workshop on “Challenges and Opportunities of Human-robot Symbiosis: from Wearable Robots to Neurorobotics” at the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2021), at a SoftBanks Robotics seminar, and at the LC2S INRS – ENSAM Metz joint lab seminar.
• Serena Ivaldi was Panelist at the HRI Pioneers Workshop 2021, Cercle Entreprise et Santé – 6eme UX Forum Exosquelettes. She gave invited talks at Queen Mary University (UK), GV lab at University of Tokyo, European Robotics Forum 2021, ICRA 2021 Workshop on Bridging the Gap between Data-driven and Analytical Physics-based Grasping and Manipulation II,HUMANOIDS 2020 Workshop on Talos, HUMANOIDS 2020 Workshop Towards Physical-social human-robot interaction, IROS 2021 Workshop on Human-like Behavior and Cognition in Robots, Workshop HRI@Montpellier “AI for improving machine interactions with humans and the environment” at LIRMM. She was a speaker at the IEEE Women In Engineering International Leadership Conference, and gave a plenary lecture at Journée FéDéV 2021 – Université Paris Saclay.

11.1.5 Leadership within the scientific community

• François Charpillet was:
  • member of the scientific committee of GDR Robotique,
  • scientific coordinator of one of the platforms founded by CPER program IT2MP.

11.1.6 Scientific expertise

• Amine Boumaza reviewed a project for ANR.
• François Charpillet was:
  • member of the admissibility committee for CRCN/ISFP Inria positions at Inria Nancy Grand Est,
  • member of the HCERES committee of LAB-STICC Bretagne.
• Serena Ivaldi was:
  • reviewer of a fellowship proposal for the Technical University of Munich (TUM),
  • evaluator for the 2020 UK best PhD award in robotics.
  • reviewer for the tenure evaluation committee of the Assistant Professor Tom Williams in Colorado School of Mines (USA).
  • committee member for two concours of enseignant-chercheur in "robotics" and "trustworthy robotics" at ENSTA Paris.
• Pauline Maurice was:
  • reviewer of a project for ANR,
  • committee member for a concours Maître de Conférences at Sorbonne Université (section 61).
• Dominique Martinez is member of the scientific council of the Institute for Information Sciences and Technologies (INS2I) of the CNRS.

11.2.1 Teaching

• Master: Alexis Scheuer, “Introduction à la robotique autonome”, 30h eq. TD, M1 informatique, Univ. Lorraine, France.
• Master: Francis Colas, “Planification de trajectoires”, 12h 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: Alexis Scheuer, “Modélisation et commande en robotique”, 20h eq. TD, M2 “Apprentissage, Vision, Robotique”, Univ. Lorraine, France.
• Master: Francis Colas, “Autonomous Robotics”, 39.5h eq. TD, M1, CentraleSupélec, France.
• Master: Pauline Maurice “Robotique pour l’industrie du futur”, 3h CM, M2 “Control Engineering”, Centrale-Supélec, France
• Master: Pauline Maurice, “Analyse Comportementale”, 10h CM/TP, M2 “Sciences Cognitives”, Univ. Lorraine, France.
• Master: Serena Ivaldi, “Analyse Comportementale”, 16h CM/TD, M2 “Sciences Cognitives”, Univ. Lorraine, France.
• Master: Amine Boumaza, “Méta-heuristiques et recherche locale stochastique”, 30h eq. TD, M1 informatique, 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”, 30h eq. TD, M1 Sciences Cognitives, Univ. Lorraine, France.
• Master: Jean-Baptiste Mouret, “Quality Diversity”, 3h (M2 Innovation, Mines Paristech)
• Reinforcement Learning Virtual School: Jean-Baptiste Mouret, “Quality Diversity optimization”, 2h & “Micro-data policy search” (2h) Program
• Tutorial, Jean-Baptiste Mouret, “Quality Diversity Optimization”, 3h, ACM GECCO 2020 (with A. Cully, Imperial College, and S. Doncieux, Sorbonne Université)

11.2.2 Supervision

• PhD: Yassine EL Khadiri, Cifre with Diatelic, “Machine learning for Ambient Assisted Living (AAL)”, 2021-08-31, Université de Lorraine, François Charpillet (advisor) 33
• PhD: Waldez Azevedo Gomes Junior, “Intelligent Human-Robot collaboration”, 2021-12-13, Université de Lorraine, Jean-Baptiste Mouret (advisor), Serena Ivaldi (co-advisor) 34
• PhD in progress : Eloise Zehnder, “Elderly-technology interaction: accessibility and acceptability of assistive technology (at) at home”, started October 2018, François Charpillet (advisor), Jérome Dinet (advisor, 2LPN).
• PhD in progress: Vladislav Tempez, “Learning to fly a micro-UAVs in highly confined environments”, started in October 2018, Jean-Baptiste Mouret (advisor), Franck Ruffier (co-advisor, CNRS/Aix-Marseille Université)
• PhD in progress : Julien Uzzan, Cifre with PSA, “Navigation of an autonomous vehicle in a complex environment using unsupervised or semi-supervised learning”, started in January 2019, François Charpillet (advisor), François Aioun (co-advisor, PSA).
• PhD in progress : Jessica Colombel, “Analysis of human movement for assistance”, started in Feburary 2019, François Charpillet (advisor) David Daney(advisor, Auctus team, Bordeaux)
• PhD in progress: Yoann Fleytoux, “Human-guided manipulation learning of irregular objects”, started in April 2019, Jean-Baptiste Mouret (advisor), Serena Ivaldi (co-advisor)
• PhD in progress: Luigi Penco, “Intelligent whole-body tele-operation of humanoid robots”, started in April 2019, Jean-Baptiste Mouret (advisor), Serena Ivaldi (co-advisor)
• PhD in progress : Nicolas Gauville, Cifre with SAFRAN, “Coordination of autonomous robots evolving in unstructured and unknown environment for Search and Rescue”, started in May 2019, François Charpillet (advisor), Christophe Guettier (co-adviser, Safran).
• PhD in progress: Yang You, “Modèles probabilistes pour la collaboration humain-robot”, started in October 2019, Olivier Buffet (advisor), Vincent Thomas (co-advisor)
• PhD in progress: Lorenzo Vianello, “Adaptation in human-robot collaboration”, started in December 2019, Alexis Aubry (advisor, CRAN, Univ. Lorraine), Serena Ivaldi (co-advisor)
• PhD in progress: Nima Mehdi, “Perception et interprétation de l’activité humaine”, started in May 2020, Francis Colas (advisor), Serena Ivaldi (co-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: Nathalie Azevedo, “Un modèle informatique biologiquement réaliste des oscillations neuronales pathologiques observées dans la maladie de Parkinson”, started in October 2020, Dominique Martinez (advisor), Laure Buhry (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), Vincent Thomas (co-advisor).

11.2.3 Juries

• François Charpillet was:
  • examiner for the HDR of Nicolas Gaud (Université de Haute Alsace)
  • reviewer for the PhD of Imen HALIMA (Université de Rennes 1 - LTSI - INSERM)
  • reviewer for the PhD of Yoan SALLAMI (Université de Toulouse Paul Sabatier, LAAS)
  • reviewer for the PhD of Kevin Osanlou (University PSL (Paris Sciences & Lettres)
  • examiner and Jury chair for the PhD of Sébastien Piedade (UNIVERSITÉ DE TOULOUSE délivré par L’INSTITUT SUPÉRIEUR DE L’AÉRONAUTIQUE ET DE L’ESPACE ISAE-SUPAERO)
• Francis Colas was:
  • reviewer for the PhD of Ali Saghiran (Université Grenoble Alpes)
• Jean-Baptiste Mouret was:
  • reviewer for the PhD of Giuseppe Paolo (Sorbonne Université)
  • reviewer for the PhD of Nemanja Rakicevic (Imperial College, UK)
  • reviewer for the PhD of Hugo Caselles (ENSTA Paristech)
  • examiner/reviewer for the PhD of Jørgen Nordmoen (University of Oslo)
  • examiner for the PhD of Maxime Thomas (Mines Paristech / Université PSL)
• Pauline Maurice was:
  • examiner for the PhD of Simon Bastide (Université Paris-Saclay)
  • examiner for the PhD of Baptiste Menges (Université de Lorraine)
  • examiner for the PhD of Quoc Hoa Bach (Sorbonne Université)
• Serena Ivaldi was:
  • examiner for the PhD of Martin Mujica (Université de Toulouse)
  • examiner for the PhD of Edoardo Lamon (University of Pisa, Italy)
  • reviewer for the PhD of Fabrizio Bottarel (University of Genova, Italy)
  • reviewer for the PhD of Francesco Ruscelli (University of Genova, Italy)
  • reviewer for the PhD of Pietro Balatti (University of Pisa, Italy)
• Dominique Martinez was :
  • examiner/chaiman for the PhD of Nicolas Morati (Université d'Aix-Marseille)

11.3.1 Articles and contents

• Pauline Maurice took part in the “Les Décodeuses du Numérique” action from CNRS-INS2I.

11.3.2 Interventions

• Serena Ivaldi and Jean-Baptiste Mouret gave talks (“TED-like”) at the Centre Prouvé of Nancy for the Futurobot event (CNRS) Program
• Lucien Renaud and Jean-Baptiste Mouret presented the ANR project Proxilearn at the Vivatech event (Porte de Versailles, Paris), invited by the Agence Innovation Defense.
• Pauline Maurice took part in the “Futurobot #AskCNRS” action from CNRS-INS2I.
• Serena Ivaldi gave a radio interview at France Culture - La Methode Scientifique, a web interview for Novembre Numérique at Institut Français de Roumanie, and was interviewd by Oggi Scienza, Les Echos and Essentiel Santé Magazine

International journals

• 3 articleO.Olivier Buffet, J.Jilles Dibangoye, A.Abdallah Saffidine and V.Vincent Thomas. Heuristic Search Value Iteration for zero-sum Stochastic Games.IEEE Transactions on Games1332021, 1-10
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• 4 articleL.Lars Fritzsche, P. E.Pavel E Galibarov, C.Christian Gärtner, J.Jonas Bornmann, M.Michael Damsgaard, R.Rudolf Wall, B.Benjamin Schirrmeister, J.Jose Gonzalez-Vargas, D.Daniele Pucci, P.Pauline Maurice, S.Serena Ivaldi and J.Jan Babič. Assessing the efficiency of exoskeletons in physical strain reduction by biomechanical simulation with AnyBody Modelling System.Wearable Technologies2June 2021
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• 5 articleW.Waldez Gomes, P.Pauline Maurice, E.Eloïse Dalin, J.-B.Jean-Baptiste Mouret and S.Serena Ivaldi. Multi-objective Trajectory Optimization to Improve Ergonomics in Human Motion.IEEE Robotics and Automation Letters71January 2022, 342 - 349
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• 6 articleP.Pierre Laclau, V.Vladislav Tempez, F.Franck Ruffier, E.Enrico Natalizio and J.-B.Jean-Baptiste Mouret. Signal-Based Self-Organization of a Chain of UAVs for Subterranean Exploration.Frontiers in Robotics and AI8April 2021
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• 7 articleC.Claudia Latella, Y.Yeshasvi Tirupachuri, L.Luca Tagliapietra, L.Lorenzo Rapetti, B.Benjamin Schirrmeister, J.Jonas Bornmann, D.Daša Gorjan, J.Jernej Čamernik, P.Pauline Maurice, L.Lars Fritzsche, J.Jose Gonzalez-Vargas, S.Serena Ivaldi, J.Jan Babič, F.Francesco Nori and D.Daniele Pucci. Analysis of Human Whole-body Joint Torques during Overhead Work with a Passive Exoskeleton.IEEE Transactions on Human-Machine Systems2021
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• 8 articleS.Stéphane Magnenat and F.Francis Colas. A Bayesian tracker for synthesizing mobile robot behaviour from demonstration.Autonomous RobotsOctober 2021
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• 9 articleR.Rui Nouchi, H.Haruka Nouchi, J.Jérôme Dinet and R.Ryuta Kawashima. Cognitive training with neurofeedback using NIRS improved cognitive functions in young adults: Evidence from a randomized controlled trial.Brain Sciences1212022
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• 10 articleM.Mario Selvaggio, M.Marco Cognetti, S.Stefanos Nikolaidis, S.Serena Ivaldi and B.Bruno Siciliano. Autonomy in Physical Human-Robot Interaction: a Brief Survey.IEEE Robotics and Automation Letters2021
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• 11 articleL.Lorenzo Vianello, J.-B.Jean-Baptiste Mouret, E.Eloïse Dalin, A.Alexis Aubry and S.Serena Ivaldi. Human posture prediction during physical human-robot interaction.IEEE Robotics and Automation Letters63July 2021, 6046-6053
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• 12 articleL.Lorenzo Vianello, L.Luigi Penco, W.Waldez Gomes, Y.Yang You, S. M.Salvatore Maria Anzalone, P.Pauline Maurice, V.Vincent Thomas and S.Serena Ivaldi. Human-humanoid interaction and cooperation: a review.Current Robotics Reports24December 2021, 441-454
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• 13 articleA. M.A Michael West, J.James Hermus, M.Meghan Huber, P.Pauline Maurice, D.Dagmar Sternad and N.Neville Hogan. Dynamic Primitives Limit Human Force Regulation during Motion.IEEE Robotics and Automation LettersJanuary 2022
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International peer-reviewed conferences

• 14 inproceedingsJ.Jonathan Ferrer-Mestres, T. G.Thomas G. Dietterich, O.Olivier Buffet and I.Iadine Chadès. K-N-MOMDPs: Towards Interpretable Solutions for Adaptive Management.35th AAAI Conference on Artificial IntelligenceVirtual, United StatesFebruary 2021
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• 15 inproceedingsL.Lars Fritzsche, C.Christian Gärtner, M.Michael Spitzhirn, P. E.Pavel E Galibarov, M.Michael Damsgaard, P.Pauline Maurice and J.Jan Babič. Assessing the efficiency of industrial exoskeletons with biomechanical modelling -Comparison of experimental and simulation results.21st Triennal Congress of the International Ergonomics Association (IEA 2021)Congress of the International Ergonomics AssociationVancouver, CanadaSpringerJune 2021, 353
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• 16 inproceedingsS.Serena Ivaldi, P.Pauline Maurice, W.Waldez Gomes, J.Jean Theurel, L.Liên Wioland, J.-J.Jean-Jacques Atain-Kouadio, L.Laurent Claudon, H.Hind Hani, A.Antoine Kimmoun, J.-M.Jean-Marc Sellal, B.Bruno Levy, J.Jean Paysant, S.Sergueï Malikov, B.Bruno Chenuel and N.Nicla Settembre. Using exoskeletons to assist medical staff during prone positioning of mechanically ventilated COVID-19 patients: a pilot study.12th International Conference on Applied Human Factors and Ergonomics (​AHFE 2021)263Advances in Human Factors and Ergonomics in Healthcare and Medical Devices: Proceedings of the AHFE 2021 Virtual Conference on Human Factors and Ergonomics in Healthcare and Medical Devices, July 25-29, 2021, USANew York, United StatesSpringerJuly 2021, 88
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• 17 inproceedingsY.Yang You, V.Vincent Thomas, F.Francis Colas and O.Olivier Buffet. Solving infinite-horizon Dec-POMDPs using Finite State Controllers within JESP.2021 IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI)Washington/virtual, United StatesIEEENovember 2021, 427-434
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National peer-reviewed Conferences

• 18 inproceedingsA.Aurélien Delage, O.Olivier Buffet and J.Jilles Dibangoye. HSVI pour zs-POSG usant de propriétés de convexité, concavité, et Lipschitz-continuité.JFPDA 2021 - Journées Francophones Planification, Décision et ApprentissageBordeaux (virtuel), FranceJune 2021, 1-14
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• 19 inproceedingsY.Yang You, V.Vincent Thomas, F.Francis Colas and O.Olivier Buffet. Résolution de Dec-POMDP à horizon infini à l'aide de contrôleurs à états finis dans JESP.JFPDA 2021 - Journées Francophones Planification, Décision et ApprentissageBordeaux (virtuel), FranceJune 2021
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Conferences without proceedings

• 20 inproceedingsE.Eloïse Dalin, I.Ivan Bergonzani, T.Timothée Anne, S.Serena Ivaldi and J.-B.Jean-Baptiste Mouret. Whole-body teleoperation of the Talos humanoid robot: preliminary results.ICRA 2021 - 5th Workshop on Teleoperation of Dynamic Legged Robots in Real ScenariosXi’an / Virtual, ChinaMay 2021
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• 21 inproceedingsJ.Jérôme Dinet, H.Hirokazu Kumazaki, A.Aurore Megny, H.Hanna Verdel, K.Kazunori Terada, S.Sylvie Vitel, F.François Charpillet, S.Stéphanie Claudel-Valentin, E.Eloïse Zehnder, J.Justine Simon and S.Sarah Lagrue. Faciliter la communication inter-personnelle des enfants avec TSA par la robotique.PRUNE II 2021 : Colloque Perspectives de Recherches sur les Usages du Numérique dans l'ÉducationPoitiers, FranceMay 2021
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• 22 inproceedingsJ.Jérôme Dinet. Le bien-être de l'élève : apports de la psychologie.Educatice / Educatec Le salon professionnel de l'innovation éducativeParis, FranceNovember 2021
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• 23 inproceedings J.Jérôme Dinet, R.Rui Nouchi, K.Kohei Sakaki, L.Laurent Dupont, L.Laure Coudrat and F.Francois Charpillet. Promoting Physical Activity for Elderly People: Towards a Hybrid Coaching? ERGO'IA 2021 Bidart, France 2021
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• 24 inproceedingsJ.Jérôme Dinet, H.Hanna Verdel, H.Hirokazu Kumazaki, K.Kazunori Terada, S.Sylvie Vitel and E.Eloise Zehnder. A Hybrid Robotics System for Children with Autism: A longitudinal Study of Social Interaction inside the Classroom.Scientific seminar at Larsen team (INRIA Grand Est)Nancy, FranceNovember 2021
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• 25 inproceedingsW.Waldez Gomes, P.Pauline Maurice, E.Eloïse Dalin, J.-B.Jean-Baptiste Mouret and S.Serena Ivaldi. Improving Ergonomics at Work with Personalized Multi-Objective Optimization of Human Movements.AHFE 2021 - 12th International Conference on Applied Human Factors and Ergonomics and the Affiliated ConferencesNew York, United StatesJuly 2021
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• 26 inproceedingsW.Waldez Gomes, P.Pauline Maurice, E.Eloïse Dalin, J.-B.Jean-Baptiste Mouret and S.Serena Ivaldi. Multi-objective Trajectory Optimization to Improve Ergonomics in Human Work Activities.Journées Nationales de la Robotique HumanoïdeAngers, FranceJune 2021
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• 27 inproceedingsW.W Gomes, P.P Maurice, N.N Settembre, J.J Theurel, L.L Wioland, J. J.J J Atain-Kouadio, L.L Claudon, B.B Chenuel, H.H Hani, A.A Kimmoun, B.B Levy, J. M.J M Sellal, J.J Paysant, S.S Malikov and S.S Ivaldi. Biomechanical effects of using a passive back support exoskeleton during prone-positioning maneuver: A pilot study.46th Congress of the Society of BiomechanicsSaint-Etienne, FranceOctober 2021
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• 28 inproceedingsA.Anuyan Ithayakumar, A.Aurélien Osswald, V.Vincent Thomas and P.Pauline Maurice. Reducing work-related physical fatigue with a collaborative robot: A decision-making approach.Journées Nationales de la Robotique Humanoïde (JNRH 2021)Angers, FranceJune 2021
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• 29 inproceedingsL.Lorenzo Vianello, J.-B.Jean-Baptiste Mouret, E.Eloise Dalin, A.Alexis Aubry and S.Serena Ivaldi. Human posture prediction during physical human-robot interaction.International Conference on Humanoid Robots, HUMANOIDS 2020Munich (Virtual), GermanyJuly 2021
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• 30 inproceedingsL.Lorenzo Vianello, J.-B.Jean-Baptiste Mouret, E.Eloïse Dalin, A.Alexis Aubry and S.Serena Ivaldi. Probabilistic estimation of postures during human-robot collaboration: an ergonomics perspective.12th International Conference on Applied Human Factors and Ergonomics and the Affiliated Conferences, AHFE 2021New York, United StatesJuly 2021
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• 31 inproceedingsE.Eloïse Zehnder, J.Jérôme Dinet and F.Francois Charpillet. Anthropomorphism, privacy and security concerns: preliminary work.ERGO'IA 2021Bidart, France2021
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• 32 inproceedingsE.Eloïse Zehnder, J.Jérôme Dinet and F.François Charpillet. Social virtual agents and loneliness: Impact of virtual agent anthropomorphism on users’ feedbacks.12th International Conference on Applied Human Factors and Ergonomics (AHFE 2021)New York, United StatesJuly 2021
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Doctoral dissertations and habilitation theses

• 33 thesisY.Yassine El Khadiri. Bayesian inference for activities of daily living detection for elderly home care applications.Université de LorraineJuly 2021
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• 34 thesisW.Waldez Gomes. Improving Ergonomics Through Physical Human-Robot Collaboration.Université de lorraineDecember 2021
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Reports & preprints

• 35 misc L.Lina Achaji, J.Julien Moreau, T.Thibault Fouqueray, F.Francois Aioun and F.François Charpillet. Is attention to bounding boxes all you need for pedestrian action prediction? July 2021
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• 36 miscJ.Jessica Colombel, D.David Daney and F.François Charpillet. On the Reliability of Inverse Optimal Control.September 2021
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• 37 miscA.Aurélien Delage, O.Olivier Buffet and J.Jilles Dibangoye. HSVI fo zs-POSGs using Concavity, Convexity and Lipschitz Properties.October 2021
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• 38 miscY.Yoann Fleytoux, A.Anji Ma, S.Serena Ivaldi and J.-B.Jean-Baptiste Mouret. Data-efficient learning of object-centric grasp preferences.December 2021
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• 39 reportS.Serena Ivaldi, P.Pauline Maurice, W.Waldez Gomes, J.Jean Theurel, L.Liên Wioland, J.-J.Jean-Jacques Atain-Kouadio, L.Laurent Claudon, H.Hind Hani, A.Antoine Kimmoun, J.-M.Jean-Marc Sellal, B.Bruno Levy, J.Jean Paysant, S.Sergueï Malikov, B.Bruno Chenuel and N.Nicla Settembre. Supplementary material to: “Using exoskeletons to assist medical staff during prone positioning of mechanically ventilated COVID-19 patients: a pilot study”.Université de Lorraine (Nancy); Inria Nancy Grand-Est2021
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• 40 miscV.Vincent Thomas, G.Gérémy Hutin and O.Olivier Buffet. Monte Carlo Information-Oriented Planning.March 2021
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• 41 miscY.Yang You, V.Vincent Thomas, F.Francis Colas and O.Olivier Buffet. Solving infinite-horizon Dec-POMDPs using Finite State Controllers within JESP.September 2021
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Other scientific publications

• 42 inproceedingsA.Amine Boumaza. Promoting Reproductive Isolation Through Diversity in On-line Collective Robotics.GECCO 2021 - Genetic and Evolutionary Computation Conference CompanionLille/Virtual, FranceJuly 2021
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• 43 inproceedingsE.Evelyn D'Elia, J.-B.Jean-Baptiste Mouret, J.Jens Kober and S.Serena Ivaldi. Learning task controllers on a humanoid robot using multi-objective optimization.ICRA 2021: 5th Full-Day Workshop on Legged Robots (Virtual)Xi'an/Virtual, ChinaJune 2021
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