2022Activity 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 47, 56. 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, 53.

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 52.

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, 53. However, current reinforcement learning algorithms require hundreds of trials/episodes to learn a single, often simplified, task 53, 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 54, 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 51. 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, 51, 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 66. 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 58. 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) 65 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 50, 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 49. 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
• News of the Year:
   

  • static walking
  • better support of the iCub robot
  • new self-collision check
  • support of AVX/SSE
  • several bug fixes and improvements
• 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 Priimitives for motion prediction, motion recognition, and learning from demonstration in robotics. It leverages the Eigen3 library.
• News of the Year:
  FIrst public release
• 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 Multi-robot and swarm

8.1.3 Multirobot and swarm

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 defended his PhD this year on 22th November.

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 Horizon Europe

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.

11.1.1 Scientific events: organisation

11.1.2 Scientific events: selection

11.1.3 Journal

11.1.4 Invited talks

• Serena Ivaldi gave an invited talk at the ICRA 2022 Workshop on Legged Robots; the European Robotics Forum 2022 Workshop "AI & Robotics benchmarking: towards harmonized evaluation strategies"; Journées Scientifique Inria on Robotics.
• Jean-Baptiste Mouret gave a keynote talk at the 16th European Workshop on Advanced Control Diagnosis (ACD), Nancy, France; an invited talk (in remote) for the University of Southern California (Distinguished Lectures Seminar Series in the Department of Computer Science); an invited talk at Huawei Research (Paris); an invited talk (in remote) for Naver Labs (Grenoble); an invited talk (in remote) at the European Robotics Forum.

11.1.5 Leadership within the scientific community

Serena Ivaldi is:

• Co-leader of the GT7 "Robotique Humanoide" of the GDR Robotique.
• Vice-President of the IEEE-RAS Members Activities Board (MAB).
• Co-chair of the IEEE-RAS ICRA (International Conference on Robotics And Automation - flagship conference of RAS) Steering Committee

11.1.6 Scientific expertise

• Francis Colas was expert for the SESAME call from Région Ile-de-France.
• Serena Ivaldi was expert for the selection of the QMUL UK best PhD thesis in robotics.
• Serena Ivaldi was reviewer of the first reporting period of the H2020 European Project FELICE (Grant Agreement number: 101017151 — FELICE — H2020-ICT-2018-20 / H2020-ICT-2020-2).
• Serena Ivaldi was international judge of the ANA Avatar XPRIZE competition on robot teleoperation.
• Jean-Baptiste Mouret was reviewer for an ERC StG proposal (remote referee).

11.1.7 Research administration

• Francis Colas is member of the Evaluation Commission of Inria.
• Francis Colas was member of the hiring committee for junior research scientists at Inria Sophia-Antipolis.
• Serena Ivaldi was member of the hiring committees of two maitre de conference positions at University of Lorraine: 27-MCF-0177 and 61-MCF-0098.
• Jean-Baptiste Mouret is the chair of the Department 5 of the LORIA UMR (Inria/CNRS/Univ. Lorraine) [since March 2022] and member of the scientific council of LORIA
• Jean-Baptiste Mouret was member of the hiring commitee for the junior research scientists at Inria Paris
• Jean-Baptiste Mouret is the scientific leader of the Creativ'Lab platform of Loria/Inria Nancy Grand Est [since March 2022]

11.2.1 Teaching

• 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.
• 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: Francis Colas, “Autonomous Robotics”, 39.5h eq. TD, M1, CentraleSupélec, France.
• Master: Serena Ivaldi, “Analyse Comportementale”, 16h CM/TD, M2 “Sciences Cognitives”, Univ. Lorraine, 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: 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”, 30h eq. TD, M1 Sciences Cognitives, Univ. Lorraine, France.

11.2.2 Supervision

• PhD: Vladislav Tempez, “Learning to fly a micro-UAVs in highly confined environments”, 2022-06-27, Jean-Baptiste Mouret (advisor), Franck Ruffier (co-advisor, CNRS/Aix-Marseille Université) 35
• PhD: Julien Uzzan, Cifre with PSA, “Navigation of an autonomous vehicle in a complex environment using unsupervised or semi-supervised learning”, 2022-11-22, François Charpillet (advisor), François Aioun (co-advisor, PSA) [REF HAL?]
• PhD: Jessica Colombel, “Analysis of human movement for assistance”, 2022-12-05, François Charpillet (advisor) David Daney(advisor, Auctus team, Bordeaux)
• PhD: Luigi Penco, “Intelligent whole-body tele-operation of humanoid robots”, 2022-06-07, Jean-Baptiste Mouret (advisor), Serena Ivaldi (co-advisor) 34
• PhD: Nicolas Gauville, Cifre with SAFRAN, “Coordination of autonomous robots evolving in unstructured and unknown environment for Search and Rescue”, 2022-11-18, François Charpillet (advisor), Christophe Guettier (co-adviser, Safran).
• PhD: Lorenzo Vianello, “Adaptation in human-robot collaboration”, 2022-11-12, Alexis Aubry (advisor, CRAN, Univ. Lorraine), Serena Ivaldi (co-advisor) [REF HAL?]
• 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: Yang You, “Modèles probabilistes pour la collaboration humain-robot”, started in October 2019, Olivier Buffet (advisor), Vincent Thomas (co-advisor)
• PhD in progress : Lina Achaji, Cifre with PSA, “Modélisation de systeèmes dynamiques par des réseaux de neurones à mémoire courte et longue : application pour la prédiction de l'état d'environnement routier”, started in March 2020, François Charpillet (advisor), François Aioun (Co-advisor, PSA).
• 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).

11.2.3 Juries

• Olivier Buffet was
  • reviewer for the PhD of Maxence Grand (Université Grenoble Alpes)
• Francis Colas was
  • invited to the PhD of Guilherme Alves (Université de Lorraine)
• Serena Ivaldi was
  • reviewer of the PhD of Jason Chemin(Université de Toulouse / LAAS)
  • examiner of the PhD of Noelie Ramuzat (Université de Toulouse / INSA Lyon / LAAS)
  • examiner of the PhD of Sebastian Walkotter (University of Uppsala, Sweden)
  • examiner of the PhD of Vincent Fortineau (Université Paris-Saclay)
• Jean-Baptiste Mouret was:
  • reviewer of the HDR of Clément Moulin Frier (Université Bordeaux / Inria Bordeaux)
  • reviewer of the PhD of Alexis Duburcq (Paris Science Lettre / Dauphine)
  • reviewer of the PhD of Philipp Kratzer (University of Stuttgart)
  • reviewer of the PhD of Alexandre Letalenet (Sorbonne Université)
  • examiner and president of the jury for the PhD of Thibault Tricard (Université de Lorraine)

11.3.1 Internal or external Inria responsibilities

• Amine Boumaza was
  • a member of the editorial board of Interstice

11.3.2 Articles and contents

• Francis Colas was interviewed for a chronicle in Science et Vie
• Serena Ivaldi was featured in. a reportage by Ludovic B: "Une journée avec #39 : une chercheuse en robotique" (youtube video, 39k views)
• Serena Ivaldi was interviewed by L'Usine Nouvelle, Industrie & Technologie, Mesures

11.3.3 Education

• Vincent Thomas
  • proposed two workshops for future teachers during journée SNT-NSI 2022: “physical engine and game design” and “path planning in mazes”

International journals

• 3 articleT.Timothee Anne, E.Eloise Dalin, I.Ivan Bergonzani, S.Serena Ivaldi and J.-B.Jean-Baptiste Mouret. First Do Not Fall: Learning to Exploit a Wall With a Damaged Humanoid Robot.IEEE Robotics and Automation Letters74October 2022, 9028-9035HALDOIback to text
• 4 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 Robotics2023HALDOI
• 5 articleN.Nicolas Gauville and F.François Charpillet. Approche locale pour l’exploration autonome d’environnements inconnus par une flottille de robots.Revue Ouverte d'Intelligence Artificielle35-62022, 625-643HALDOIback to text
• 6 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 - 349HALDOIback to text
• 7 articleP.Pauline Maurice, F.Félix Cuny-Enault and S.Serena Ivaldi. Influence of a passive back support exoskeleton on simulated patient bed bathing: Results of an exploratory study.ErgonomicsOctober 2022, 1-15HALDOIback to text
• 8 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, 5HALDOI
• 9 articleL.Lorenzo Vianello, W.Waldez Gomes, F.Freek Stulp, A.Alexis Aubry, P.Pauline Maurice and S.Serena Ivaldi. Latent ergonomics maps: Real-time visualization of estimated ergonomics of human movements.Sensors2211June 2022, 3981HALDOIback to text
• 10 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 Letters72January 2022, 2391 - 2398HALDOI

International peer-reviewed conferences

• 11 inproceedingsL.Lina Achaji, T.Thierno Barry, T.Thibault Fouqueray, J.Julien Moreau, F.Francois Aioun and F.François Charpillet. PreTR: Spatio-Temporal Non-Autoregressive Trajectory Prediction Transformer.2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)Macau, ChinaIEEE2022, 2457-2464HALDOIback to text
• 12 inproceedingsL.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?2022 IEEE Intelligent Vehicles Symposium (IV)Aachen, GermanyIEEE2022, 895-902HALDOIback to text
• 13 inproceedingsA.Amine Boumaza. Seeking Specialization Through Novelty in Distributed Online Collective Robotics.EvoApplications 2022 - 25th International Conference on the Applications of Evolutionary ComputationLecture Notes in Computer Science13224Madrid, SpainApril 2022, 635–650HALDOI
• 14 inproceedingsJ.Jessica Colombel, D.David Daney and F.François Charpillet. On the Reliability of Inverse Optimal Control.2022 International Conference on Robotics and Automation (ICRA)ICRA 2022 - IEEE International Conference on Robotics and AutomationPhiladelphia, United StatesMay 2022, 8504-8510HALback to text
• 15 inproceedingsE.Evelyn D'Elia, J.-B.Jean-Baptiste Mouret, J.Jens Kober and S.Serena Ivaldi. Automatic Tuning and Selection of Whole-Body Controllers.2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)Kyoto, JapanOctober 2022HALback to text
• 16 inproceedingsK.Kourosh Darvish, S.Serena Ivaldi and D.Daniele Pucci. Simultaneous Action Recognition and Human Whole-Body Motion and Dynamics Prediction from Wearable Sensors.IEEE-RAS 21st International Conference on Humanoid Robots (Humanoids)2022 IEEE-RAS 21st International Conference on Humanoid Robots (Humanoids)Ginowan, JapanIEEENovember 2022, 488-495HALDOIback to text
• 17 inproceedingsJ.Jérôme Dinet. "Would you be friends with a robot?”: The impact of perceived autonomy and perceived risk.AHFE 2022 - 13th International Conference on Applied Human Factors and ErgonomicsNew York, United StatesJuly 2022HAL
• 18 inproceedingsJ.Jérôme Dinet and R.Rui Nouchi. Promoting Physical Activity for Elderly People with Immersive Virtual Reality (IVR).AHFE 2022 - 13th International Conference on Applied Human Factors and ErgonomicsNew York, United StatesJuly 2022HAL
• 19 inproceedingsY.Yoann Fleytoux, A.Anji Ma, S.Serena Ivaldi and J.-B.Jean-Baptiste Mouret. Data-efficient learning of object-centric grasp preferences.ICRA 2022 - IEEE International Conference on Robotics and AutomationPhiladelphia, United StatesMay 2022HALback to text
• 20 inproceedings M.Melanie Jouaiti, E.Eloïse Zehnder and F.François Charpillet. The Sound of Actuators in Children with ASD, Beneficial or Disruptive? Lecture Notes in Computer Science ICSR 2022 : 14th International Conference on Social Robotics Florence, Italy Springer December 2022 HAL back to text
• 21 inproceedingsA.Anji Ma, Y.Yoann Fleytoux, J.-B.Jean-Baptiste Mouret and S.Serena Ivaldi. VP-GO: A 'Light' Action-Conditioned Visual Prediction Model for Grasping Objects.ICARM 2022 - IEEE International Conference on Advanced Robotics and MechatronicsICARM 2022 - IEEE International Conference on Advanced Robotics and MechatronicsGuilin, ChinaJuly 2022HALback to text
• 22 inproceedingsG.Glenn Maguire, N.Nicholas Ketz, P.Praveen Pilly and J.-B.Jean-Baptiste Mouret. A-EMS: An Adaptive Emergency Management System for Autonomous Agents in Unforeseen Situations.Lecture Notes in Computer ScienceTAROS 2022 - Towards Autonomous Robotic SystemsLNAI - 13546LNAI - Annual Conference Towards Autonomous Robotic SystemsAbingdon, United KingdomSpringer International PublishingSeptember 2022, 266-281HALDOI
• 23 inproceedingsN.Nima Mehdi, V.Vincent Thomas, S.Serena Ivaldi and F.Francis Colas. Simultaneous Pose and Posture Estimation with a Two-stage Particle Filter for Visuo-inertial Fusion.ICARM 2022 - IEEE International Conference on Advanced Robotics and MechatronicsIEEE International Conference on Advanced Robotics and Mechatronics (ICARM 2022)Guilin, ChinaJuly 2022HALback to text
• 24 inproceedingsJ.Jérôme Truc, P.-T.Phani-Teja Singamaneni, D.Daniel Sidobre, S.Serena Ivaldi and R.Rachid Alami. KHAOS: a Kinematic Human Aware Optimization-based System for Reactive Planning of Flying-Coworker.IEEE International Conference on Robotics and Automation (ICRA 2022)Philadelphia, United StatesMay 2022HALDOI
• 25 inproceedingsE.Eloïse Zehnder, J.Jérôme Dinet and F.François Charpillet. Perception of physical and virtual agents: exploration of factors influencing the acceptance of intrusive domestic agents.2022 31st IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)31st IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)Napoli, ItalyIEEE2022, 1050-1057HALDOIback to text
• 26 inproceedingsE.Eloïse Zehnder, M.Melanie Jouaiti and F.François Charpillet. Evaluating Robot Acceptance in Children with ASD and their Parents.Lecture Notes in Computer Science14th International Conference on Social RoboticsFlorence (IT), ItalySpringerDecember 2022HALback to text

National peer-reviewed Conferences

• 27 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 Kingdom2023HAL
• 28 inproceedingsA.Aurélien Delage, O.Olivier Buffet and J.Jilles Dibangoye. Max-Min Optimization for Lipschitz-Continuous Functions.ROADEF 2022 - 23ème congrès annuel de la Société Française de Recherche Opérationnelle et d'Aide à la DécisionVilleurbanne - Lyon, FranceFebruary 2022, 1-2HALback to text
• 29 inproceedingsY.Yang You, V.Vincent Thomas, R.Rachid Alami, F.Francis Colas and O.Olivier Buffet. Planification robuste pour la collaboration homme-robot.Actes des Journées Francophones Planification, Décision et Apprentissage (JFPDA)Journées Francophones Planification, Décision et Apprentissage (JFPDA)Saint Etienne (FR), FranceJune 2022HALback to text

Conferences without proceedings

• 30 inproceedings J.Jérôme Dinet. L’Interaction Humain-Robot (IHR) et les enfants porteurs de TSA : quoi, pourquoi, pour qui ? Journée d'études 2021 - Les apports des technologies numériques au développement et aux apprentissages des enfants porteurs de TSA Nancy, France January 2022 HAL
• 31 inproceedingsR.Raphaël Lartot, J.Jean Michenaud, A. O.Alexandre Oliveira Souza, P.Pauline Maurice, S.Serena Ivaldi and F.François Charpillet. Torque prediction for active exoskeleton control using ProMPs.JNRH 2022 - Journées Nationales de la Robotique HumanoïdeAngers, FranceJuly 2022HAL
• 32 inproceedingsA.Alexandre Oliveira Souza, J.Jean Michenaud, R.Raphaël Lartot, J.Jordane Grenier, F.François Charpillet, P.Pauline Maurice and S.Serena Ivaldi. Simulating Upper Body Exoskeleton on a Digital Human Model.JNRHAngers, FranceJuly 2022HALDOI

Doctoral dissertations and habilitation theses

• 33 thesisS.Serena Ivaldi. From humans to humanoids: learning and interaction for human-humanoid collaboration.Université de Lorraine (UL)November 2022HAL
• 34 thesisL.Luigi Penco. Whole-body Teleoperation of Humanoid Robots.Université de LorraineJune 2022HALback to text
• 35 thesisV.Vladislav Tempez. Learning of an optimal control law for a small quadrotor flying in a cylindric pipe.Université de LorraineJune 2022HALback to text

Reports & preprints

• 36 miscJ.Jessica Colombel, D.David Daney and F.François Charpillet. Holistic vision of Inverse Optimal Control.September 2022HAL
• 37 miscA.Aurélien Delage, O.Olivier Buffet, J.Jilles Dibangoye and A.Abdallah Saffidine. HSVI Can Solve Zero-Sum Partially Observable Stochastic Games.October 2022HALDOIback to text
• 38 miscW.Waldez Gomes, P.Pauline Maurice, J.Jan Babič, J.-B.Jean-Baptiste Mouret and S.Serena Ivaldi. In a Collaborative Co-manipulation, Humans Have a Motor Behaviour Similar to a Leader.February 2022HAL
• 39 miscL.Lorenzo Vianello, W.Waldez Gomes, P.Pauline Maurice, A.Alexis Aubry and S.Serena Ivaldi. Cooperation or collaboration? On a human-inspired impedance strategy in a human-robot co-manipulation task.February 2022HAL
• 40 miscL.Lorenzo Vianello, S.Serena Ivaldi, A.Alexis Aubry and L.Luka Peternel. The effects of role transitions and adaptation in human-cobot collaboration.2022HAL

Other scientific publications

• 41 inproceedingsN.Nima Mehdi, V.Vincent Thomas, S.Serena Ivaldi and F.Francis Colas. Duration Models for Human Activity Prediction in Industry-Like Tasks.JJCR 2022 : Journée des Jeunes Chercheurs et Jeunes Chercheuses en Robotique 2022Paris, FranceNovember 2022HALback to text
• 42 inproceedingsA.Aya Yaacoub, V.Vincent Thomas, F.Francis Colas and P.Pauline Maurice. Probabilistic planning for cobots to mitigate fatigue in repetitive comanipulation.JJCR 2022 : Journée des Jeunes Chercheurs et Jeunes Chercheuses en Robotique 2022Paris, FranceNovember 2022HALback to text
• 43 inproceedingsJ.Jacques Zhong, V.Vincent Weistroffer, P.Pauline Maurice, C.Claude Andriot and F.Francis Colas. Interacting with a torque-controlled virtual human in virtual reality for ergonomics studies.IEEE VR 2022 - 29th IEEE Conference on Virtual Reality and 3D User InterfacesChristchurch, New ZealandMarch 2022HALDOIback to text