3.1.1 Scientific Context

The purpose of this axis is to provide metrics to assess human behavior. Our human study specifically focuses on industrial operators. We assume the following working hypotheses: the operator's task and environmental conditions are known and circumscribed; the operator is trained in the task, production tools and safety instructions; the task is repeated with more or less frequent intervals. We aim to analyze the following:

• the physical and cognitive fragility of operators to meet assistance needs;
• cognitive biases and physical constraints leading to a loss of the operator safety;
• ergonomics, performance and acceptance of the production tool.

In the industrial context, these questions are tackled through the fields of work ergonomics and cognitive sciences. Four main axes are typically addressed: physiological/biomechanical, cognitive, psychological and sociological studies. We particularly focus on the biomechanical, cognitive and psychological aspects, as described by the ANACT 28, 29. The aim is to translate these factors into metrics, optimality criteria or constraints to better analyze, design and control the collaborative robots.

A review of ergonomic workstation evaluation helps in positioning our desired contributions in robotics. Ergonomists evaluate the gesture through the observation of the workstations and, generally, through questionnaires. This requires long periods of field observation, followed by analyses based on ergonomic grids (e.g. RULA 43, REBA 35, LUBA 39, OWAS 38, ROSA 56,...). Until then, the use of more complex measurement systems was reserved for laboratories, particularly in biomechanical studies. The advent of cost-effective and minimally intrusive motion capture sensors (Inertial Measurement Units, RGB-D cameras), coupled with advancements in computer vision algorithms, now enables on-site data collection to assess workers' gestures, postures, movements, and physiological states. Some of these systems can be permanently integrated into production lines without disrupting workflow, while simultaneously evaluating worker well-being through biomechanical and physiological metrics. This facilitates longitudinal motion capture studies, similar to the evolution of maintenance practices from reactive to predictive.

Ergonomic robotics has recently taken an interest in this new evaluation paradigm to adapt the robot behavior to reduce ergonomic risks. This ergonomic adaptation complements the conventional approaches that only consider the performance of the action produced by the human in interaction with the robot. However, ergonomic criteria are usually based on the principle that the comfort positions are distant from the human joint limits. These notations are compatible with an observation of the human operator through the eye of the ergonomist. In practice, such evaluations are inaccurate and subjective 59. Moreover, they only consider quasi-static human positions, without taking into account the evolution of the person's physical, physiological and psychological state. We aim to extend this approach to more reliable and comprehensive ergonomic metrics with musculoskeletal, fatigue, metabolic consumption modelling. The repetition of gestures, and the solicitation of muscles and joints, are questions that must complete these analyses. A method used by ergonomists to limit biomechanical exposures is to increase variations in motor stress by rotating tasks 57. However, this type of extrinsic method is not always suitable in the industrial context 42 where we place our research efforts.

Through these human analyses, the Auctus team aims to revise the use of collaborative robots in the workplace to vary the operator's environment and encourage more appropriate motor strategies. Biomechanical studies of the intrinsic variability of the motor system allowed by the joint redundancy of the human body should result in the alternation of postures, movements and muscle activity observed in the individual to perform a requested task 57. This variation leads to differences between the motor coordination used by each operator, and conveys the notion of motor strategy 36. We aim to provide exhaustive studies of motor strategies in industrial setups.

The cognitive dimension of ergonomics must also be addressed in our approach to reduce the mental workload and foster the wellness of the worker. We believe that known sensorimotor strategies can be a physically quantifiable reflection of the operator's cognitive state. For example, human motion measurements can be used to predict fatigue 54 and, therefore, adjust the robot's assistance. A key challenge here is to better analyze human manual expertise (dexterous and cognitive) to adapt the human-robot interaction. The expertise embodies the operators' decision-making process while perceiving, understanding, and anticipating their gestures to preserve their safety, comfort, and performance in the task. We aim to adapt and refine known human cognitive models (multisensory perception 33, situation awareness 32) to infer the influence of the task context and environment on operator behavior.

3.1.2 Methodology

How can we observe, understand and quantify human sensorimotor and cognitive strategies to better design and control the behavior of the cobotic assistant?

When we study systems of equations (kinematic, static, dynamic, musculoskeletal, etc.) to model human behavior, several problems appear and explain our methodological choices:

• the large dimension of the problems to be considered, due to the human body complexity (eg. joint, muscle redundancy);
• the distribution of anthropometries, force capacities, and fatigue resistance among workers, for example, size, the muscle mass, the possible geometric configurations on a workstation (set of possible trajectories, postures, and placements of the operator), and the forces needed to be produce to execute a task;
• the uncertainties in the measurement and the model approximations.

The idea is to start with a description of redundant workspaces (geometric, static, dynamic...). We use set-theory approaches, based on interval analysis 58, 48, which meet the variability requirement and cope with model and measurement uncertainties. Another advantage of such techniques is that they allow the results to be certified, which is essential to address safety issues. Some members of the team have already achieved success in mechanical design for performance certification and robot design 45. By extending these set-theory approaches to our problem, a mapping of ergonomic, efficient, and safe movements can be obtained, in which we project the operators' motor strategies. Biomechanical, ergonomic, and cognitive metrics can, then, be defined and evaluated to quantify the human behavior in specific work situations.

It is therefore necessary to:

• model human capabilities, both at the musculoskeletal and the perceptive/cognitive levels, to allow for global, yet detailed, analyses as well as efficient integration of such knowledge in the control of the collaborative robots.
• contribute to accurate representations of the shoulder joint involved in most cobotic interactions and worker efforts. Its complex range of motion and the numerous muscles involved make proper shoulder modeling a significant challenge for musculoskeletal (MSK) models 10.
• propose new ergonomic, biomechanical, robotic, and cognitive indices that will link different types of performances while taking into account the influence of fatigue, stress, level of expertise, etc.;
• divide the task and the gesture into homogeneous phases: this process is complex and depends on the type of studied index and the techniques being used. We are exploring several methods: inverse optimal control, learning methods, techniques from signal processing;
• develop interval extensions of the proposed indices. The indices are not necessarily the result of a direct model, and algorithms must be developed or adapted to compute them (calculation of manipulability, Uncontrolled Manifold, etc.);
• Aggregate proposals into a dedicated interval-analysis library for human behavior studies (use of and contribution to the existing ALIAS-Inria and the open-source IBEX library).

The major contribution of the methodology is to embrace in the same model the measurement uncertainties (important for on-site use of measurement equipment), the variability of tasks and trajectories (proper to dexterous industrial operations), and the physiological characteristics of the operators (critical adaptability to every individual). The originality of the approach is to combine biomechanical, ergonomic and cognitive metrics with the usual performance indices to build a comprehensive and objective analysis of human behavior.

Other avenues of research are being explored, particularly around the inverse optimal control 49, to project human movements based on the performance or ergonomic indices. Such a projection would offer new interpretations and enhance the analysis of human behaviors.

Finally, the development of a population-scale musculoskeletal model database and the application of anthropometry-robust optimal control represents a new aera of research that the team will explore by 2025. This approach would further explore the inherent variability within human workforces. By constructing a diverse virtual population through statistical and biomechanical principles, we will be able to leverage predictive simulation to identify broadly applicable movement strategies and minimize potential strain across a spectrum of worker morphologies and capabilities. This dual approach moves beyond traditional, one-size-fits-all ergonomic assessments, enabling the development of personalized or, at minimum, broadly robust recommendations for safer and more efficient work practices.

3.2.1 Human-Robot interaction

The first step to couple the operator and cobot together at work is to provide interaction modalities through which the agents can communicate and coordinate. The interaction can be direct, where the robot and operator act together in the same shared environment, or the operator can remotely perform the task with the robot through a teleoperation system (which reflects the remote interaction and potentially corrects for punctual weaknesses). The level and type of human-robot interactions are chosen with respect to the task, the context, or other human factors. The challenge is, then, to predict the joint human-robot behavior and capabilities for each interaction situation and collaborative context.

The formal computation of joint human-robot capabilities can be given thanks to the models and evaluation indices presented in Axis 1. We can focus on quantifying how the interaction with the robot will impact the human sensorimotor strategy (changes in the posture, positions, forces, etc., induced by the robot) and recomputing metrics such as human fatigue and motor variability53 and the Mover project. We can further use the biomechanical and robotic models to consider a unified operator-robot entity and to compute their joint abilities (e.g. common human-robot force capabilities 55).

Developing human cognitive and sensorimotor models to account for the effect of the human-robot interaction could provide a valuable tool to evaluate cobotic systems and collaborative works. However, the accuracy of these models must be addressed. We wish to understand how the robot influences the operator's work, and thus how his mental model of the task evolves according to the interactions with the robot. The challenge is, then, to predict the behavior of the operator that takes into account his cognition in the interaction situations. Preliminary literature results have shown that key cognitive mechanisms in human teaming may transfer to human-robot collaboration, such as joint human-robot action representation 30 or coordination mechanisms 47. However, the situation awareness of the operator is modified by the interaction with the robot 52. Developing a joint mental model that accurately captures the human-robot interaction can later guide the design of relevant interaction modalities to improve the team's understanding 2.

3.2.2 Cobot adaptive assistance

Taking into account the coupling between the operator and the robot at the control level is also central to the team's objectives. We wish to demonstrate how a collaborative robot can be used to mediate between a control objective that optimizes task performances, safety and comfort (what we consider as the expertise trinity), on one hand, and the action model of the human interacting with the robot (the inferred human intent), on the other hand. Such an arbitration in the control law adapts the robot's assistive behavior to better collaborate with the operator. This shared-autonomy concept is the focus of part of our research. It can range from a discrete task allocation between the agents to an effectively shared task 46.

We are strongly confident that the notion of expertise is central to adjusting the cobot behavior. The robot controller is designed to increase the level of expertise in the operator/robot team: it optimizes the human-centered metrics (safety criteria, biomechanical and cognitive comfort, etc.) and provides a gain in performances (joint human-robot capabilities). But it also aims at preserving the operator's particular expertise and know-how at the center of the activity. Manual expertise of highly skilled operators needs to be analyzed respectively on its dynamic aspects and on the ability to synchronize with other operators in the environment. Understanding better the expertise is envisioned as a way to alleviate the operators of repetitive and easy operations while maintaining their ability to perform expert gestures based on the complexity of the task.

Furthermore, this research axis raises the question of the modification of the work induced by collaborative robots for expert operators. While the overall goal is to make use of robots to punctually or continuously improve the work conditions of these operators (and not to replace them), the presence of these robots necessarily impacts the work referential and thus the expertise itself. One of the central questions, yet to be tackled, relates to the original and core part of the expertise that should remain unchanged. The proposed modeling of the operator/robot coupling and interaction is the first avenue to predict possible changes in the expertise. It can be input to the controller to constraint the robot to let the operator make the expert decisions naturally.

3.3.1 Architectural design

Is it necessary to cobotize, robotize or assist the human being? Which mechanical architecture meets the task challenges (a serial cobot, a specific mechanism, an exoskeleton)? What type of interaction (H/R cohabitation, comanipulation, teleoperation)? These questions are the first requests from our industrial partners. For the moment, we have few comprehensive methodological answers to these requests. Choosing a collaborative robot architecture is a difficult problem 40. It becomes even more complex when the architectural design is approached from concurrent cognitive ergonomic, biomechanical and robotic perspectives. There are major methodological and conceptual differences in these areas. It is, therefore, necessary to bridge these representational gaps and to propose a global and generic approach that takes into consideration the expectations of the robotician to model and formalize the general properties of a cobotic system as well as those of the ergonomist to define the expectations in terms of an assistance tool.

To do this, we propose a user-centered design approach, with a particular focus on human-system interactions. From a methodological point of view, this requires to develop a structured experimental approach. It aims at characterizing the task to be carried out ("system" analysis) but also at capturing the physical markers of its realization (required movements and efforts, ergonomic stress, etc.). This specification must be done through the prism of a systematic study of the exchanged information (type and modality) needed by humans to perform the considered task. Based on these analyses, the main challenge is to define a decision support tool for the choice of the robotic architecture and the specifications of the role assigned to the robot and the operator as well as their interactions.

The evolution of the chosen methodology is for the moment empirical, based on the user cases regularly treated in the team (see sections on contracts and partnerships).

The process can be summarized through the following steps:

• identify expert or difficult jobs on industrial sites. This is done through visits and exchanges with our partners (manager, production manager, ergonomist...);
• select some challenging use cases to be studied and, then, observe the operator in its ecological environment. Our motion capture tools allow us to produce a force-motion analysis, based on previously defined ergonomic criteria, and a physical evaluation of the task in terms of expected performances, from both experiments and simulations. In parallel, an evaluation of the operator's expertise and cognitive strategy is throught questionnaires;
• synthesize these observatory results into design requirements to deduce: the robotic architectures to be initiated, the key points of human-robot interaction to be developed, and the difficulties in terms of human factors to be taken into account.

The different human and task analyses take advantage of the expertise available within the AUCTUS team. The team has already worked on the currently dominant approach: the use of human models to design the cobotic cell through virtual tools 3. We would like to gradually introduce the additional evaluation criteria presented above. However, the very large dimensions of the treated problems (modeling of the body's degrees of freedom and the constraints applied to it) makes it difficult to carry out a certified analysis. We choose to go through the calculation of the human workspace and performances, which is not yet done in this field. The idea here is to apply set theory approaches, using interval analysis as already discussed in section 3.1.2. The goal is, then, to extend the human constraints to intervals, which integrate the model variability, and to play them in virtual reality during simulations of the tasks. This would allow the operator to check his trajectories and scenarios not only for a single case study but also for sets of cases. For example, it can be verified that, regardless of the bounded sets of simulated operator physiological capabilities, the physical constraints of a simulated trajectory are not violated. Therefore, the assisted design tools certify cases of use as a whole. Moreover, the intersection between the human and robot workspaces/capabilities provides the necessary constraints to certify the feasibility of a task in the interaction situation. Overall, integrating human and task-related physical constraints in the design process brings to better cobotic systems. In the future, we will similarly develop tools to include human cognitive markers in the design approach.

This research line merges the contributions of the other axes, from the analysis of the human behavior and capabilities in its environment for an identified task, the prediction of the effects of the interaction/coupling strategy with the robot, to the choice of a mechanical architecture from the resulting design constraints. The proposed task-oriented and human-centered methodology is perfectly integrated into an Appropriate Design approach. It can be used for the dimensional design and optimization of robots, again based on interval analysis. The challenges are the change of scale in models that symbiotically consider the human-robot pair, the uncertain, flexible and uncontrollable nature of human behavior, and the many evaluation indices needed to describe them.

It is worth noting that we aim to develop a global mechatronic design approach, which would build upon the design constraints to specify the robot hardware and controller at once. The chosen set-theory computational methodology is particularly appropriate to meet this objective since the interval-based representation of the design constraints can be directly and equally used to set the control constraints.

3.3.2 Control design

The control laws of collaborative robots from the major robot manufacturers differ little or not at all from the existing control laws in the field of conventional industrial robotics. Security is managed a posteriori, as an exception, by a security PLC / PC. It is therefore not an intrinsic property of the controller. This strongly restricts possible physical interactions 1 and leads to suboptimal operation of the robotic system. It is difficult in this context to envision tangible human-robot collaboration. Collaborative operation requires, in this case, a control calculation that integrates safety and ergonomics as a priori constraints.

The control of truly collaborative robots in an industrial context is, from our point of view, underpinned by two main issues. The first one is related to the macroscopic adaptation of the robot's behavior according to the phases of the production process. The second one is related to the fine adaptation of the degree and/or nature of the robot's assistance according to the ergonomic state of the operator. If this second problem is part of a historical dynamics in robotics that consists in placing safety constraints, particularly those related to the presence of a human being, at the heart of the control problem 3444, 37, it is not approached from the more subtle point of view of ergonomics where the objective cannot be translated only in terms of human life or death, but rather in terms of long-term respect for their physical and mental integrity. Thus, the simple and progressive adoption by a human operator of the collaborative robot intended to assist him in his gesture requires a self-adaptation in the time of the command. This self-adaptation is a fairly new subject in the literature 50, 51.

At the macroscopic level, the task plan to be performed for a given industrial operation can be represented by a finite-state machine. To avoid increasing the human's cognitive load by explicitly asking him to manage transitions for the robot, we propose to develop a decision algorithm that would ensure discrete transitions from one task (and the associated assistance mode) to another based on an online estimate of the current state of the human-robot couple. The associated scientific challenge is to establish a link between the robot's involvement and a given working situation. We propose an incremental approach to learn this complex relationship. Its first stage will consist of identifying the general and relevant control variables to conduct this learning in an efficient and reusable way, regardless of the particular calculation method of the control action. Then, physically realistic simulations and real-world experiments will be used to feed this learning process.

To handle mode transitions, we propose to explore the richness of the multi-tasking control formalism under constraints 41. It would ensure a continuous transition from one control mode to another while guaranteeing compliance with a certain number of robot control constraints. Some of these constraints convey the ergonomic specifications and are dependent on the state of the robot and of the human operator, which, by nature, is difficult to predict accurately. We propose, again, to exploit the interval-analysis paradigm to efficiently formulate ergonomic constraints robust to the various existing uncertainties.

Purely discrete or reactive adaptation of the control law would make no sense given the slow dynamics of certain physiological phenomena such as fatigue. Thus, we propose to formulate the control problem as a predictive problem where the impact of the control decision at a time $t$ is anticipated at different time horizons. This requires a prediction of human movement and knowledge of the motor variability strategies it employs. This prediction is possible based on the supervision at all times of the operational objectives (task in progress) in the short term. However, it requires the use of a virtual human model and possibly a dynamic simulation to quantify the impact of these potential movements in terms of performances, including ergonomics. It is impractical to use a predictive command with simulation in the loop with an advanced virtual manikin model. We, therefore, suggest adapting the prediction horizon and the complexity of the corresponding model to guarantee a reasonable computational complexity.

7.1.1 Qontrol

• Name:
  Quadratic Optimization coNTROL
• Keywords:
  Robotics, Control, Optimisation
• Scientific Description:
  Generic expression of a robot control problem using constrained optimization in a dynamic environment, applicable to robots that can be controlled in terms of torque, speed or position.
• Functional Description:
  Qontrol is a tool for the generic formulation of robotic control problems in the form of constrained optimization problems. It is initially intended for fixed-base polyarticulated robots. It allows to easily create tasks and constraints in the control law.
• News of the Year:
  Declaration to the Program protection office. Appears in "transfert challenge" kind of project (awaiting for finding) Appears in a european project (awaiting for finding)
• URL:
  https://­auctus-team.­gitlabpages.­inria.­fr/­components/­control/­qontrol/­index.­html
• Contact:
  Lucas Joseph
• Participants:
  Lucas Joseph, Vincent Padois

7.1.2 ROS Haptic Teleop

• Keywords:
  Telerobotics, Haptic
• Functional Description:
  ros_haptic_teleop defines the following parameters : scaling factor and transformation matrix between two robot and haptic frames, homing poses for both robots, and control gains used for homing of the robots or to maintain the haptic device pose. An integrated RViz plugin handles the different modes of operation (teleop, homing, maintain) from buttons of the visual interface. The package subscribes to the pose and velocity topics of the robot and the haptic device. It transforms these references (scaling and rotation) in the other robot's space, and publishes the transformed quantities (scale_pose and scale_velocity). These transformed pose/velocity are input to the robot motion controller (external). It also subscribes to the external force sensed by the robot and publishes the transformed value to generate a direct haptic feedback on the interface (external force controller). It interfaces with external haptic controllers, by subscribing to their command topics, such as the guiding force (active constraint) or the shared pose and velocity (blending), and by integrating them to the robots' control inputs. These external references are enabled through activation parameters (enable_ac and enable_blending).
• URL:
  https://­gitlab.­inria.­fr/­auctus-team/­people/­alexisboulay/­teleoperation_ros/­ros_haptic_teleop
• Contact:
  Alexis Boulay
• Participants:
  Alexis Boulay, Margot Vulliez, Lucas Joseph

7.1.3 Chai3D Haptic Driver

• Keywords:
  Telerobotics, Haptic
• Functional Description:
  This driver is based on the chai3d library. It handles any haptic device which has been defined in chai3d/src/devices. Falcon device of Novint and Omega/Sigma/Delta devices of Force Dimension are available by default. Device specifications such as the maximal force, stiffness, and damping, are made accessible as ros messages. This driver provides cyclical exchanges with the opened haptic device. It publishes pose and velocity data on ros topics. It subscribes to the desired force topic to provide the haptic feedback through the interface. It also manages the device's gripper/buttons through dedicated ros topics.
• URL:
  https://­gitlab.­inria.­fr/­auctus-team/­people/­margotvulliez/­chai3d_haptic_driver
• Contact:
  Margot Vulliez
• Participants:
  Margot Vulliez, Alexis Boulay

7.1.4 Active Constraint

• Keywords:
  Haptic guidance, Haptic, Telerobotics
• Scientific Description:
  The implementation of an active constraint or virtual guide in a teleoperation context with a haptic interface aims to encourage operators to follow a desired behaviour while allowing a certain freedom of action. This package also includes the integration of a generic formula enabling a continuous transition between the different types of generic guides (potential fields, spring-damper and guide tube).
• Functional Description:
  active_constraint defines the following parameters: stiffness and damping factors for 3 different types of guidance (potential field, spring-damper and guide tube), as well as threshold distances and parameters enabling guidance to be generated using a generic approach. The integration of a RVIZ plugin means that it is possible to change on the fly between each guide and to modify the above-mentioned parameters. The package subscribes to the position and velocity topics of the robot and the scaled haptic interface in the robot workspace. These positions and speeds are used to generate a guidance force which is sent to the communication hub linked to the haptic interface. This package also allows the definition of obstacles and targets required to generate guidance, the relative poses are then published.
• URL:
  https://­gitlab.­inria.­fr/­auctus-team/­people/­alexisboulay/­teleoperation_ros/­active_constraint
• Contact:
  Alexis Boulay
• Participants:
  Alexis Boulay, Margot Vulliez, David Daney

7.1.5 MPC based shared control

• Name:
  Model Predictive control based Shared Control
• Keywords:
  Robotics, Telerobotics, Optimal control, Shared control, Collaborative robotics
• Functional Description:
  This software is designed for Human-Robot Interaction (HRI) applications, enabling Smooth integration of human input with an existing assistance system. The controller dynamically blends both inputs, ensuring that human commands are refined while preserving the overall task objectives. By enforcing workspace constraints and respecting the robot’s physical capabilities, the system guarantees safe and feasible motion execution. Additionally, it actively mitigates unwanted oscillations caused by abrupt or jerky human inputs, resulting in smoother and more stable robot behavior.
• URL:
  https://­gitlab.­inria.­fr/­auctus-team/­people/­eliojabbour/­mpcontroller.­git
• Contact:
  Elio Jabbour

7.1.6 mpc_tutorial

• Keywords:
  Robotics, Optimal control
• Functional Description:
  This repository provides a set of python scripts solving on toy optimal control problems (simple pendulum, cartpole, etc.). The goal is to familiarize with the core concepts and methods from Optimal Control theory (continous and discrete time).
• URL:
  https://­github.­com/­skleff1994/­mpc_tutorial
• Contact:
  Sebastien Kleff

7.1.7 force_feedback_mpc

• Keywords:
  Robotics, Control, Optimization
• Functional Description:
  This library is basically an extension of the Crocoddyl optimal control library: it implements custom action models in C++ allowing to quickly prototype MPC schemes with force feedback.
• URL:
  https://­github.­com/­machines-in-motion/­force_feedback_mpc/­tree/­constraints
• Contact:
  Sebastien Kleff

7.1.8 POP-ART

• Name:
  Performant Optimization Problems for Advanced Robot Trajectories
• Keywords:
  Robotics, Motion control, Trajectory Generation, Optimization
• Functional Description:
  A set of methods dedicated to advanced robot motion generation through efficient constrained optimization formulation, coded and examplified in Python. More precisely a ROS workspace for the QP-based control of robots using Pinocchio and including : - a simple QP based controllers expressed at the velocity level - a simple QP based controllers expressed at the acceleration level - a cartesian jogging demo using an acceleration QP and considereing joint limits - a trajectory tracking controller at the Cartesian level - a trajectory tracking controller at the Cartesian level including path constraints and a target constant velocity - a joint states estimation algorithm using a QP expressed at the jerk level or a Savitsky-Golay filter
• Release Contributions:
  Initial version
• URL:
  https://­gitlab.­inria.­fr/­auctus-team/­people/­sebastiendignoire/­protected/­pop-art
• Contact:
  Vincent Padois

7.2.1 Maze teleoperation

Participants: Lucas Joseph.

A new platform has been developed to demonstrate teleoperation modes. Participants are tasked with maneuvering a vial through a maze using a teleoperating device. Two types of haptic guidance were provided for the task: one without any guidance and another utilizing the guiding tubes tested by Alexis Boulay in his recent experiments. While the platform was initially designed for general public use, it could be improved for research applications. Many software developed by the team for haptic guidance are used for this platform.

7.2.2 evaLuation Of hapTic guidance on teleopeRation (LOTR)

Participants: Alexis Boulay, Margot Vulliez, David Daney.

Teleoperation allows remote task execution in environments inaccessible or hazardous to humans. While offering significant benefits, the physical separation between the operator and the workspace often leads to reduced performance and increased workload due to a lack of feedback. Haptic guidance addresses these challenges by using haptic devices to apply forces that direct the operator toward a task goal. By offering different reaching tasks' scenario in a vertical farming context, this setup was used to compare conventional haptic guidance methods from the literature, to evaluate a unified haptic guidance model called the ruling guidance, and to highlight the impact of environmental conditions and the guidance models on the human-robot interaction, in terms of task performance and safety, as well as human comfort and trust.

7.2.3 expePacbot

Participants: Loic Mazou.

To study the effects of responsibility, body position, and eye position during a collaborative task with a robot, a platform has been designed for creating Lego patterns in collaboration with a robot. This platform was developed as part of the Pacbot project and is intended for use by researchers involved in this joint collaboration. The platform uses software developed by Loïc to detect the Lego configurations via a standard RGB camera (oakInterface and happypose_exemples). It also features a Panda robot from Franka Emika (panda_qontrol_ws) or a Yumi robot (yumi_qontrol_ws), which performs the collaborative tasks.

7.2.4 Bipetto: a serial-parallel robotic leg prototype

Participants: Virgile Batto, Margot Vulliez.

The platform consists of the prototype of one serial-parallel robotic leg. It is located at LAAS-CNRS and is part of our collaboration with the Gepetto team. It was designed by using the co-design method 8.4.6 to optimize the dynamic performances of a bipedal walking robot. This platform aims at evaluating the leg's design, in terms of motion accuracy and force capability, before we make a complete prototype of the bipedal robot. It will also validate the simulation and the robot models used during the optimization process.

8.1.1 Attention Sharing Handling Through Projection Capability Within Human-Robot Collaboration

Participants: Benjamin Camblor, Lucas Joseph, Jean-Marc Salotti, David Daney.

The link between situation awareness (SA) and the distribution of human attention, has been explored within a human robot collaboration framework. According to (Endsley, 1995), SA is divided into three levels: perception, comprehension and projection. It is involved in the process of making decisions and carrying out actions in a dynamic environment. This work investigates three hypotheses. First, that the ability to project a robot's future actions improves performance in a collaborative task. Second, that the more participants are involved in tasks in a collaborative environment, the better their SA will be. Finally, that the use of a robot's non-verbal communication motions attracts a participant's attention more promptly than if the robot remains motionless. A within-participants study has been designed to investigate our three hypotheses. Participants were asked to perform a collaborative task with a robot (Franka Emika Panda robotic arm). It required them to assist the robot at different moments while they were engaged in a distracting task that was catching their attention (tower of Hanoi puzzle). These moments could either be anticipated and taken into account in the human decision-making and action loop or not. Lastly, the robot could either use non-verbal communication gestures to draw human attention or not. The results have demonstrated the significance of considering the human capability to project a robot next actions in their own personal attention management. Moreover, the subjective measures showed no difference in the assessment of SA, in contrast to the objective measures, which are in line with our second hypothesis. Finally, it seems that standing stationary can be considered a gesture of non-verbal communication. In the present work, robot waiting was more salient in capturing human attention when the robot remained motionless rather than making a signaling motion. The most important part of the work had been carried out last year but the valorization occured in 2024 with Camblor's thesis and a publication in the International Journal of Social Robotics 7.

8.1.2 Decision making with Delphi method

Participants: Jean-Marc Salotti, Ephraim Suhir.

An international collaboration has been implemented with Ephraim Suhir, senior researcher from Portland State University, expert in risks modeling and statistics, in order to address the problem of defining and combining complex heterogeneous criteria for the assessment of a teleoperated robotic task, considering task performance, reliability, safety and ergonomics. The Delphi method, a process of arriving at group consensus by providing experts with rounds of questionnaires, as well as the group response before each subsequent round, has been applied. The task was to teleoperate a heavy mobile robot in a shed using a tablet-style control interface. A consensus emerged among 8 experts, who selected the criteria of duration, number of trajectory corrections and feeling of the operator. Weights have been proposed for each criterion and statistical tools have been used to evaluate the consensus. This work has been published in the Journal of Field Robotics 12.

8.1.3 Modeling of Human visuo-haptic perception

Participants: Rémi Lafitte, Margot Vulliez.

Reliable visual and haptic feedback are known to improve teleoperation tasks. Better understanding human perception could help to develop feedback strategies that better inform the human operator during human-robot interaction. In parallel, psychophysic experiments have suggested that the brain combines visual and haptic estimates of environmental properties (e.g., object size) in a statistically optimal fashion. Whether this sensory integration still holds in a more challenging environment, such as for teleoperation, remains unknown. We are developing a psychophysic experiment, in healthy adults performing teleopration estimation tasks, to test this hypothesis. We will notably assess if integrating the perceptual estimates of an individual operator in the feedback process can help the latter to better perform a common telopration pick-and-place task. The project is conducted in collaboration with the CeRCA-CNRS at the University of Poitiers.

8.1.4 Legibility and predictability of haptic guidance in robot teleoperation

Participants: Benjamin Camblor, Margot Vulliez.

Haptic guidance is a method to assist a human operator performing tasks in teleoperation through force feedback. Taking into account human factors and individual preferences is crucial when designing such haptic assistance. It can modify the perception and understanding that humans have of the robot's assistance behaviour, i.e. the cognitive transparency of the robot.Specifically, making the robot actions more predictable and/or more legible can highly impact the understanding of the robot's behavior during human-robot interaction. The predictability of an action is linked to its similarity to the action that the human agent would expect for a given objective (action deduced from its goal). Conversely, an action is legible when it enables the human to predict the objective or intention it expresses (goal deduced from the action). The project aims at evaluating the impact of different haptic guidance trajectories on the legibility and predictability of the robot's assistance. It has been initiated with a literature review on legibility, predictability and cognitive transparency of robot motion and haptic feedback in teleoperation. We are currently designing an experimental protocol and platform to study how different haptic guidance can be more or less legible and predictable, during assisted teleoperation scenarios.

8.1.5 Understanding Agency in Human-Robot Collaboration

Participants: Alicia Barsacq, David Daney, Jean-Christophe Sarrazin.

In cognitive neuroscience, and more specifically in neuroergonomy, recent studies have demonstrated that a system's transparency depends on the user's ability to activate agency mechanisms, enabling control over the system's behavior. Agency appears to be strongly linked to prediction mechanisms, as reported in numerous studies.

The thesis of Alicia Barsacq investigates agency in the context of a collaborative task between a robot and a human agent, with a particular focus on the impact of different robotic assistive modalities. The objective is to determine the most effective feedback to stimulate the agent's predictive mechanisms and enhance collaboration. The experimental study will primarily explore haptic feedback as a teleoperative assistive modality.

This thesis is co-supervised with ONERA as part of the PEPR O2R program

8.2.1 Study of Motor Variability

Participants: David Daney, Vincent Padois, Pauline Maurice, Jonathan Savin.

This long term research project led to a main action in 2024: the analysis of the experimental results obtained during the experimental campaign of 2023 (MOVER). These results are encouraging as they demonstrate that motor variability depends on some features of the task, especially pace and direction of the movement. Moreover, the observed motor variability is high enough to induce changes in biomechanical risk factors as estimated through standard ergonomic scores 18. The analysis of the results will be pursued in 2025, potentially within the framework of the INTRO project submitted to the ANR yearly call in collaboration with INRS and the LARSEN team in Nancy.

8.2.2 Searching for best-fitting musculoskeletal models approximating an individual's upper limb force capacities

Participants: Gautier Laisné, Jean-Marc Salotti, Nasser Rezzoug.

8.3.1 Re-expression of manual expertise through manual control of a teleoperated system

Participants: Erwann Landais, Vincent Padois, Nasser Rezzoug.

In the thesis of Erwann Landais which was defended in December 21, we studied how teleoperation can allow for the remote expression of technical gestures, with chemistry as a potential applicative domain. Indeed, teleoperation enables a task to be carried out remotely by a human expert. This remote control is often a guarantee of greater safety and comfort, or simply of feasibility in hazardous environments. However, it can also mean a loss of efficiency, or added complexity. To avoid these pitfalls, it is necessary to consider 1) what constitutes an operator's expertise for a given task, 2) the constraints encountered in carrying it out, and 3) the form of a teleoperation system adapted to it. An example of a task that could benefit from teleoperation is the task of finding solvents for chemical compounds, which is one of Syensqo's areas of expertise. This involves characterizing the solubility of a solute in a set of solvents, in order to determine the optimum solvent for that solute. This task, based on visual, cognitive and manual expertise, is performed by a small number of expert technicians. Performing this task relies on an empirical, tedious and sometimes dangerous process, motivating the distancing of technicians from the experimental environment through robotic assistance. Using this task as a case study, the thesis of Erwann Landais aimed at answering the following questions : on the one hand, how can an operator's expertise be preserved when performing a task using a teleoperated system ? And on the other hand, how can the suitability of a teleoperation solution for performing an expert task be assessed ? To answer these questions, a broad-spectrum literature review was carried out and two experimental studies were conducted. While the first study studied semi-automatic manipulation protocols, in the second study, which restults were analysed in 2024, a robot was designed to achieve a range of motion similar to that observed in technicians, and intuitive interfaces were used to define the desired movement of the vial in real time. The study shows that controlling the robot via these interfaces does not achieve an efficiency similar to that of the manual mode. However,the performance achieved is encouraging, and the study identifies several avenues of improvement for the efficient and reliable deporting of the technical gesture in chemistry. Finally, beyond the application framework, this work establishes a comprehensive methodology for evaluating the performance of teleoperation modalities.

8.3.2 A comparative study of haptic guidance methods, toward a generic guidance model

Participants: Alexis Boulay, Benjamin Camblor, Margot Vulliez, David Daney.

This work is within the framework of the collaboration with the Farm3 company. Performing remote tasks through a teleoperation system can be assisted through haptic guidance, a force feedback computed based on a virtual geometric constraints to help the user to follow a given behavior (task trajectory, safety area,...). If such haptic guidance methods are commonly used in the literature, no study has clearly defined which guidance model is the most suitable with respect to different tasks, environments, or user expertise.

8.3.3 Adaptive haptic guidance during human-robot interaction

Participants: Alexis Boulay, Anqiu Hu, Margot Vulliez, David Daney.

Conventional haptic guidance are computed from a path that should help the human while performing the task. This guidance path is often fixed and preplaned in advance based on an approximate knowledge of the environment, estimated from the sensors of the robot. It, therefore, may be imperfect with respect to the task. Moreover, it does not take into account the individual variability of the human performing the task. We have started to develop new mechanisms to deform the guidance path based on the user interaction, during the execution of the task, whether from a geometric or a dynamic point of view. Different deformation models are compared, such as elastic-band paths or Bezier curves. A parallel study has been conducted to extract new guidance trajectories from the human input motion, for the robot to learn new tasks by demonstration. We compared two different dynamic representations of task motion that can be used in the learning process: Dynamical Movement Primitive and Probabilistic Movement Primitive.

8.3.4 Effect of an Exoskeleton on Precision in Simulated Welding

Participants: Alicia Barsacq.

Alicia Basarq participated in a study conducted in collaboration with the Wearable Lab of the Sant'Anna School of Pisa. This research focuses on the effect of an upper-limb exoskeleton on precision during a simulated welding task. A scientific communication has been written to highlight these findings.

8.4.1 Parametric Trajectories and Measurement Error in Inverse Optimal Control

Participants: Ahmed-Manaf Dahamni, David Daney, François Charpillet.

Inverse optimal control (IOC) is a growing field of research that has gained significant traction in human motion prediction and learning. However, current methods for solving IOC have several problems ranging from computation time to handling noisy data. This work builds upon the foundation laid in 31 in their attempt to explain the IOC through the lens of Karush–Kuhn–Tucker (KKT) optimality conditions and the concept of singularity curves. This work, presented in 13 tackles the issue of finding metrics to relate parametric representations of trajectories to discrete measurement error.

8.4.2 On the robustness of Projected Inverse Optimal Control

Participants: Ahmed-Manaf Dahamni, David Daney, François Charpillet.

Direct optimal control allows generating a trajectory by optimizing a cost function. We continue to work on the inverse approach, which aims to identify the weights of this function from an observed trajectory. In recent years, we have proposed a method called Projected Inverse Optimal Control (PIOC), which reduces the problem to a projection onto a hypersurface describing the singularities of a matrix. Its mathematical characterization can take different forms, and its evaluation can lead to numerical instabilities as the dimensionality of the observed trajectory representation increases. This year, we have worked on various solutions to evaluate this singularity and its derivative in order to enhance the robustness of PIOC.

8.4.3 Model Predictive Control for shared human-robot tasks

Participants: Elio Jabbour, Margot Vulliez, Vincent Padois.

Over the last year, we developped a Model Predictive Control-based Blending (MPC-B) approach for shared control in teleoperation, aimed at overcoming limitations of traditional Linear Blending (LB). The method frames blending as a constrained optimization problem, ensuring compliance with task and safety constraints while enabling smooth control transitions. By predicting robot motion over a receding horizon, MPC-B anticipates and enforces constraints on position, velocity, and acceleration.

A comparative evaluation was conducted through simulations and real-world pick-and-place experiments using a Franka Emika Panda robot. Results showed that MPC-B significantly enhanced task accuracy, safety, and motion continuity while reducing human effort. By formulating blending as a constrained optimization problem, the MPC-B method ensured compliance with position, velocity, and acceleration constraints, offering smoother, more predictable robot motions. The evaluation metrics highlighted the improved repeatability, constraint adherence, and overall system performance under MPC-B. Additionally, users experienced greater trust and comfort in shared control with MPC-B, as evidenced by increased reliance on assistance.

The study shows the potential of model predictive blending for safer and more efficient human-robot collaboration, with future work focusing on adaptive authority allocation and uncertainty environment modelling.

A technical report on this work is available in 24.

8.4.4 Dynamic authority distribution in haptic shared control

Participants: Jacques Zhong, Margot Vulliez.

Teleoperation of robotic manipulators aims at combining the expertise of the human operators and the assistive capabilities of the robotic system, in a variety of industrial activities where the human should stay afar from the work area for better security and ergonomics. In this context, an important challenge lies in how two agents (i.e. the human and the assistance) share the control of the teleoperated robot. While the assistance tries to predict the intent and adapt to the human actions, the human and assistance behaviors may differ due to their own physical capabilities, different task strategies, or incomplete models of the environment. Therefore, we want the resulting robot behavior to minimize the conflicts between the human and the assistance. In the shared control community, this problem is described as an arbitration problem, which consists in correctly adjusting the level of control authority between the human and the assistance. This authority level should be adjusted dynamically during the activity, as the human may need to take over the assistance, for example when there is an unexpected obstacle or a change of target. This work was initiated through an exploratory study of possible methods to solve this authority-distribution problem :

• a bibliographic research on shared control, shared autonomy and more specifically learning-based approaches that dynamically adapts the authority level based on past interactions between the human and the robot through the teleoperation device. Later works aim at exploring whether learning-based approaches can help with finding the complex relationship between human-assistance behaviors and the authority level.
• the implementation of approaches based on control theory and optimization, to dynamically adjust the authority level, done in a simple simulation. The implemented methods will further be tested and validated on a real teleoperation system to assess their performances.

8.4.5 Towards interaction control through tactile feedback

Participants: Sebastien Kleff, Vincent Padois.

Sebastien Kleff , in collaboration with Vincent Padois , submitted a Marie Skłodowska-Curie Action (MSCA) project on Tactile Adaptation for Contact-based Teamwork and Interactive Control Strategies (TACTICS). This project is based on the observation that so-called "collaborative" robots lack the adaptability required for effective human-robot collaboration (HRC). In particular, their physical interaction skills are limited to the preservation of human safety, which drastically reduces their pertinence and team-working capabilities. The proposed research projectr targets this fundamental limitation by augmenting their sensing apparatus with tactile sensing. Specifically, TACTICS will develop a control architecture for HRC that systematically exploits tactile information to adapt online the robot behavior in contact with changing environments. This project will 1) extend modern optimal control with tactile feedback, 2) learn a multimodal human intention estimation framework 3) design tactile-informed adaptation mechanisms. This interdisciplinary project is expected to result in a versatile control architecture allowing cobots to adapt to the high variability of tools and human behaviors encountered in industrial manufacturing.

In order to build the TACTICS project upon solid methological tools, three main actions have been led:

• in collaboration with the Machines in Motion Laboratory at NYU (Armand Jordana, Avadesh Meduri and Ludovic Righetti), the Willow team at Inria Paris (Justin Carpentier) and the Gepetto team at LAAS-CNRS (Nicolas Mansard), Sebastien Kleff benchmarked the numerical optimal control solver based on Sequential Quadratic Programming mim_solvers against state-of-the art. A manuscript was re-submitted to IEEE Transactions on Robotics (T-RO) in October 2024 with additional results addressing the editor's comments, such as a benchmarking of our Quadratic Program (QP) solver against state-of-the-art QP solvers HPIPM and OSQP. S. Kleff actively contributed to the writing of the re-submission and the implementation of the benchmarks.
• Sebastien Kleff worked on extending the force feedback MPC framework developed during his PhD research to constrained tasks. This work aims at producing additional experimental results in order to re-submit our manuscript to IEEE T-RO. In particular, we developed and maintained the "force_feedback_mpc" library 7.1.7 to allow force constraints and collision avoidance constraints between the robot and workspace obstacles. An experimental campaign was carried out by S. Kleff and A. Jordana at New York University in November 2024 in order to collect those additional results using the Machines in Motion Laboratory infrastructure.
• Sebastien Kleff started a state-of-the-art on both control methods for tactile interaction and tactile sensing technologies. Several contacts and scientific discussions have been undertaken with leading research teams in this domain.

8.4.6 Optimization and design of bipedal-robot leg architectures

Participants: Virgile Batto, Margot Vulliez.

The legged-robot codesign PhD project, in collaboration with the Gepetto team at LAAS-CNRS, aims at developing AI-based tools to help in designing a new leg architecture for a dynamic walking robot.

8.4.7 Online approach to near time-optimal task-space trajectory planning

Participants: Antun Skuric, Nicolas Torres, Vincent Padois, Lucas Joseph, David Daney.

Pursuing the work on robot capacities evaluationn undertaken in the thesis of Antun Skuric , we have developed an online approach to near time-optimal task-space trajectory planning. This work tackles the problem of underestimating true robot capabilites, leading to suboptimal use of time, energy and material resources. Indeed, conforming to safety standards often limits collaborative robots' performance and size, restricting their applications despite their capabilities. Planning their motions in human environments involves a trade-off between optimal trajectory planning and quick adaptation to dynamic, unstructured spaces. Traditional trajectory planning methods either use simplified robot models and sacrifice robot's abilities for computational efficiency, or exploit robots' abilities fully but have high computational complexity and rely on substantial pre-computation. In this we work, we introduce an approach for trajectory planning that exploits robot's full motion abilities while planning on-the-fly. In each step of the trajectory execution, it evaluates robot's movement ability using polytope algebra and calculates a time-optimal Trapezoidal Acceleration Profile (TAP) on the remaining trajectory. The method is shown to be near time-optimal (around 5% slower trajectories) by benchmarking it against the state-of-the-art time-optimal method TOPP-RA. It is also shown to be able to reach higher velocities (able to plan up to 100% of the robot's kinematic limits) while at the same time having lower tracking error (under 4mm) than traditional Cartesian Space planning methods. A mock-up experiment demonstrates its efficiency in collaborative waste sorting using a Franka Emika Panda robot.

This work 27 has been recently re-submitted to the IEEE Transactions on Robotics journal.

8.4.8 Solving semi-constrained end-effector path planning problems

Participants: Guillaume De Mathelin De Papigny, Vincent Padois.

As a follow-up to the Plan de Relance with the SME Aerospline, we have continued our work on proposing a compuationally efficient algorithm to solve semi-constrained end-effector path planning problems. This method, aiming at finding motion solution for robots evolving in highly constrained environements while presenting some level of task redudancy, has been formalized and a technical report on this work is available in 25.

8.4.9 Performant Optimization Problems for Advanced Robot Trajectories

Participants: Sébastien Dignoire, Vincent Padois.

As a wrap-up to the Plan de Relance with the start-up Fuzzy Logic, we have developped a set of examples implementing methods dedicated to advanced robot motion generation through efficient constrained optimization formulation. More precisely, these tools come as ROS workspace for the QP-based control of robots using Pinocchio 7.1.8.

10.1.1 Inria associate team not involved in an IIL or an international program

SHAARE Inria-KAIST associate team

Participants: Huseyin Tugcan Dinc, Dong-Hyeon Kim, Kwang-Hyun Lee, Jee-Hwan Ryu, Alexis Boulay, Jacques Zhong, Vincent Padois, David Daney, Margot Vulliez.

• Title:
  Shared Haptics for Augmented Assistive Robot Expertise
• Duration:
  2024 ->
• Coordinators:
  Margot Vulliez (Auctus) and Jee-Hwan Ryu (IRiS lab director @KAIST)
• Partners:
  • IRiS lab - Korea Advanced Institute of Science and Technology (Daejeon, South Korea)
• Summary:
  Haptic teleoperation is a promising method to enable Humans and robots to jointly perform an activity. The human operator can safely and remotely control the robot while receiving feedback on the task interaction. Recent shared-autonomy concepts have been proposed in the literature to transfer part of the task from the human operator to the robotic agent to better assist him. Scientific challenges to tackle in such shared control support the need for a generic shared haptic framework. It will merge both haptic guidance (force feedback that assists the human to perform the task) and blending methods (control strategy that combines the human inputs and robot assistive skills). This global framework is the main research direction of the SHAARE associate team and will be our common baseline to share different developments and control techniques. The joint works specifically aims at augmenting robot assistive behavior in haptic teleoperation by improving haptic guidance, by dynamically distributing the authority between the human-robot agents, and by transferring expert skills from the human to the robot.

10.2.1 Visits of international scientists

10.2.2 Visits to international teams

Research stays abroad

Participants: Alexis Boulay.

• Visited institution:
  IRiS lab - KAIST
• Country:
  South Korea
• Dates:
  September to November 2024
• Context of the visit:
  In haptic guidance, the user is virtually guided on a pre-planed assistance trajectory, set by the user beforehand or generated offline from a task planner. However, it usually remains fixed during the task execution while the environment is uncertain and dynamically changing. The associate team develops approaches to deform the guidance trajectory based on the user interaction during the task, to improve the user comfort and trust in the assistance. The research visit focusqes on these approaches, and was done in collaboration with Kwang-Hyun Lee (Postdoc @KAIST) and Jee-Hwan Ryu (Full Professor @KAIST)
• Mobility program/type of mobility:
  Collaborative research visit - SHAARE associate team

10.3.1 Other european programs/initiatives

Assistance Generation Techniques for multipurpose robot

Participants: Olivier David (CEA LIST), Margot Vulliez, Vincent Padois.

This collaborative work with the CEA LIST is part of the EUROfusion program, and explore robotic teleopration solutions to help in the maintenance of the future DEMO fusion-energy power plant. In the context of unplanned tasks, this work intends to provide a set of assistive generic tools and robot control solutions to help the operator to overcome unexpected situations in maintenance operations with a multipurpose robotic manipulator. User-interactive tools should provide a way to the operator to program remote handling tasks on the fly according to the context, the type and geometry of the equipment needing maintenance. Given the user-specified task, predictive controllers aim at locally computing the robot motion with respect to both the task trajectory and the human input motion (from the teleoperation device). Haptic feedback would be generated to guide the human on the planned trajectory.

Project in a nutshell:

• Consortium: CEA LIST, AUCTUS@Inria
• Funding: EUROfusion
• Duration: 2024

11.1.1 Scientific events: organisation

11.1.2 Scientific events: selection

11.1.3 Journal

11.1.4 Invited talks

11.1.5 Leadership within the scientific community

• Vincent Padois is the representative of the Inria Program Agency and member of the creation and steering committee of the PEPR Robotics Acceleration.
• David Daney is the Deputy Head of Science at Inria centre at the University of Bordeaux, April-July 2024.
• David Daney is the Head of Science at Inria centre at the University of Bordeaux since July 2024.
• David Daney is a member of the Scientific Council of the GDR Robotics, the main organization for robotics researchers in France.

11.1.6 Scientific expertise

The Auctus team is involved in the "Aquitaine robotics" cluster, which brings together robotics players in Nouvelle-Aquitaine. David Daney and Jean-Marc Salotti respectively represent Inria and Ensc on the board of directors. David Daney is a member of the executive board. David Daney and Jean-Marc Salotti are respectively president and vice-president of the labelling committee which promotes all robotics projects for the Nouvelle-Aquitaine region.

11.1.7 Research administration

• Virgile Batto is Doctoral students' representative at the EdSys doctoral school since 2023
• David Daney is a member of of the Inria Evaluation Committee since April 2024.
• David Daney is a member of the France 2030 Robotics Committee.
• David Daney is member of the "Commission des Emplois de Recherche" for the Inria centre at the University of Bordeaux.
• David Daney is the coordinator of 3 working groups (GT) on the evaluation and on the creation of 3 Inria Projet-Teams for the Inria Evaluation Committee.
• David Daney is a member of the executive board of R4, a regional robotics network involving 12 research entities in the region of Nouvelle-Aquitaine, France.
• David Daney is the principal investigator of ANR Pacbot
• David Daney is the principal investigator of DTR Grip4All ANR/France 20230
• Vincent Padois is since september 2022 head of the "Commission des Développements Technologiques" for Inria centre at the University of Bordeaux.
• Jean-Marc Salotti is in charge of international relationships at ENSC.
• Margot Vulliez is member of the "Commission des Emplois de Recherche" for Inria centre at the University of Bordeaux, since January 2024.
• Margot Vulliez is the principal investigator of the ANR ASAP-HRC (2021-2026) and AAPR Nouvelle-Aquitaine Perception-HRI (2022-2027) projects.
• Margot Vulliez is the principal investigator of the SHAARE Inria-KAIST associate team (2024-2026) with the IRiS lab.

11.2.1 Teaching

• Licence: Virgile Batto , Robotics, 15h eqTD, L2, BUT GE2I, IUT of Toulouse, France.
• Licence: Margot Vulliez , Mechanical Design, 24h éqTD, L1, BUT GMP, IUT of Bordeaux, France.
• Licence: Margot Vulliez , Design Projects, 13h éqTD, L2, BUT GMP, IUT of Bordeaux, France.
• Master: Alexis Boulay , Projet Informatique Individuel, 6h éqTD, M1, Ensc, Bordeaux INP, France.
• Master: Alexis Boulay , Introduction à ROS2, 24h éqTD, M2, Enseirb, Bordeaux INP, France.
• Master: Benjamin Camblor , Fonction cognitives en situation et Handicap, 11.25 éqTD, M1 (S7), Master de Sciences cognitives et Ergonomie, Université de Bordeaux, France.
• Master: Gautier Laisné , Bases de l’intelligence artificielle, 16h éqTD, M1, École Nationale Supérieure de Cognitique.
• Master: Gautier Laisné , Projet de fin d’études, 9h éqTD, M2, École Nationale Supérieure de Cognitique.
• Master: Lucas Joseph , ROS, 44h éq TD, CESI, Bordeaux, France.
• Licence: Nasser Rezzoug , Biomécanique, 60h éqTD, L2, Faculté des Sciences du Sport (FSS), Université de Poitiers, France.
• Licence: Nasser Rezzoug , Biomécanique, 10.5h éqTD, L1, Faculté des Sciences du Sport (FSS), Université de Poitiers, France.
• Licence: Nasser Rezzoug , Biomécanique de la déficience motrice et du vieilliessement, 23h éqTD, L3, Faculté des Sciences du Sport (FSS), Université de Poitiers, France.
• Licence: Nasser Rezzoug , Aspects biomécaniques du handicap, 18h éqTD, L3, Faculté des Sciences du Sport (FSS), Université de Poitiers, France.
• Master: Nasser Rezzoug , Modalités de prescription de l'activité physique, 18h éqTD, M1, FSS Master APAS/IRHPM, Université de Poitiers, France.
• Master: Nasser Rezzoug , Analyse cinematique et dynamique du mouvement, 20h éqTD, M1, FSS Master APAS/IRHPM, Université de Poitiers, France.
• Master: Nasser Rezzoug , Métrologie, 16h éqTD, M1, FSS Master APAS/IRHPM, Université de Poitiers, France.
• Master: Nasser Rezzoug , Ergonomie du poste de travail, 23h éqTD, M1, FSS Master APAS/IRHPM/ et SFA Ingénierie biomécanique, Université de Poitiers, France.
• Diplome d'Etat de masseur kinesitherapeute : Nasser Rezzoug , Biomécanique, 21h éqTD, CHU et Université de Poitiers, France.
• Master: Jean-Marc Salotti , Bases de l'intelligence artificielle (ENSC 2A), 40h eqTD.
• Master: Jean-Marc Salotti , Apprentissage Automatique (ENSC 2A), 25h eqTD.
• Master: Jean-Marc Salotti , Interactions Humains Robots (ENSC 3A), 32h eqTD.
• Master: Jean-Marc Salotti , Planning (parcours robotique ENSC-ENSEIRB 3A), 15h eqTD.
• Master: Jean-Marc Salotti , Facteurs Humains et Ingénierie Cognitique (ENSC 3A), 28h eqTD.
• Master: Jean-Marc Salotti , supervision of projects and internships (ENSC 1A, 2A, 3A)and jury for oral presentations, 100h eqTD.
• Master: Vincent Padois , Literature review - What, Why and How?, 20h éqTD, M2, Enseirb/Ensc, Bordeaux INP, France.
• Master: Vincent Padois , Maintenance du futur - Cours introductif, 2h éqTD, M1, ENSPIMA, Bordeaux INP, France.
• Master: Vincent Padois , Maintenance du futur - Introduction à la Robotique, 10h éqTD, M2, ENSPIMA, Bordeaux INP, France.
• Master: Vincent Padois , Introduction à la Robotique, 2h éqTD, M1, Université de Bordeaux, France.
• Master: David Daney , Interactions Humains Robots, 6h eqTD, M2, Ecole Nationale Supérieure de Cognitique / Bordeaux INP, France.
• Master: David Daney , Mathématiques pour la robotique, 30h eqTD, M2, Enseirb/Ensc, Bordeaux INP, France.
• Master: David Daney , oral expression, 6h eqTD, M2, Enseirb/Ensc, Bordeaux INP, France.
• Master: Alicia Barsaq , Structures arborescentes, 46h eqTD, M1, Enseirb, Bordeaux INP, France.
• Master: Ahmed-Manaf Dahmani , Interaction humain robot, 16h eqTD, M2, Ensc Bordeaux INP, France.
• License : Ahmed-Manaf Dahmani , Initiation à l'algorithmique, 49h eqTD, L3, Enseirb, Bordeaux INP, France.

11.2.2 Supervision

11.2.3 Juries

11.3.1 Productions (articles, videos, podcasts, serious games, ...)

• Margot Vulliez : Video storytelling, robotics research career, Numérixplore, September 2024, Poitiers.
• Margot Vulliez : Podcast speaker for "Désassemblons le numérique", Episode 10, Inria Bordeaux research center, September 2024, Talence.

11.3.2 Participation in Live events

11.3.3 Others science outreach relevant activities

• Auctus team, Live demonstration of robot teleoperation in a maze with haptic guidance at Bordeaux City Hall within the framework of the Journées RobNA – Robotique en Nouvelle Aquitaine, co-organized by the Regional Research Network on Robotics, Pey-Berlan, Bordeaux, October 2024
• Lucas Joseph and Vincent Padois , Live demonstration of robot capacities vizualization using augmented reality at Humanoids 2024, Nancy, November 2024
• Alexis Boulay and Elio Jabbour , AI4Industry, Running a stand demonstrating haptic teleoperation, Enseirb, Talence, Jan 2024
• Vincent Padois Live demonstration of robot teleoperation in a maze with haptic guidance during the event "Portes Fermées" at Inria centre at the University of Bordeaux, December 2024

International journals

• 7 articleB.Benjamin Camblor, D.David Daney, L.Lucas Joseph and J.-M.Jean-Marc Salotti. Attention Sharing Handling Through Projection Capability Within Human-Robot Collaboration.International Journal of Social RoboticsFebruary 2024HALDOIback to text
• 8 articleÉ.Étienne Fournier-Aubret, A.Aurélie Landry, B.Beatrice Piras, D.Damien Pellier, H.Humbert Fiorino, D.David Daney and C.Christine Jeoffrion. Proof of concept of a cobotic system in a constrained work environment.Applied Ergonomics125May 2025, 104472HALDOI
• 9 articleC. A.Camilo Andres Rey Arias, W.Wouter Weekers, M.Marco Morganti, V.Vincent Padois and A.Alessandro Saccon. Refined Post-Impact Velocity Prediction for Torque-Controlled Flexible-Joint Robots.IEEE Robotics and Automation Letters2024HALDOI
• 10 articleN.Nasser Rezzoug, A.Antun Skuric, V.Vincent Padois and D.David Daney. Simulation Study of the Upper-Limb Isometric Wrench Feasible Set With Glenohumeral Joint Constraints.Journal of Biomechanical Engineering1472February 2025, 024501HALDOIback to text
• 11 article J.-M.Jean-Marc Salotti. Can humanity survive to a giant comet impact? Acta Astronautica March 2024 HAL DOI
• 12 articleJ.Jean‐marc Salotti and E.Ephraim Suhir. Collaborative Robotics: Application of Delphi Method.Journal of Field RoboticsNovember 2024HALDOIback to text

International peer-reviewed conferences

• 13 inproceedingsA.-M.Ahmed-Manaf Dahmani, D.David Daney and F.François Charpillet. Parametric trajectories and measurement error in inverse optimal control.MMT Symposium 2024 - Mechanism and Machine Theory SymposiumGuimaraes, PortugalJune 2024HALback to text
• 14 inproceedingsV.Vincent Fortineau, V.Vincent Padois and D.David Daney. Prediction of pose errors implied by external forces applied on robots: towards a metric for the control of collaborative robots.IEEE International Conference on Robotics and Automation - ICRA 2024Yokohama, JapanMay 2024HAL
• 15 inproceedingsE.Erwann Landais, N.Nasser Rezzoug and V.Vincent Padois. Re-expression of manual expertise through semi-automatic control of a teleoperated system.Human Factors in Robots, Drones and Unmanned Systems; 15th International conference on Applied Human Factors and Ergonomics and the Affilated ConferencesAHFE 2024 - 15th International Conference on Applied Human Factors and Ergonomics138Nice, France2024HALDOI
• 16 inproceedingsM.Margot Vulliez and O.Oussama Khatib. A New Compact Paired-Parallel Architecture for Haptic Transparency.Advances in Robot Kinematics 2024Springer Proceedings in Advanced RoboticsAKR 2024 - 19th International Symposium on Advances in Robot Kinematics31Springer Proceedings in Advanced RoboticsLjubljana, SloveniaSpringer Nature SwitzerlandJuly 2024, 288-296HALDOI

Conferences without proceedings

• 17 inproceedingsA.Alexis Boulay, D.David Daney and M.Margot Vulliez. Ruling guidance an adaptive and dynamic haptic guide model.MMT Symposium BOOK OF ABSTRACTSMMT Symposium 2024 - Mechanism and Machine Theory SymposiumGuimaraes, PortugalJune 2024, p.383-384HAL
• 18 inproceedingsJ.Jonathan Savin, R.Raphaël Bousigues, P.Pauline Maurice, V.Vincent Padois, N.Nasser Rezzoug and D.David Daney. Motor variability in human-robot interaction during an assisted trajectory-tracking task: preliminary results from the MOVER experiment.Humanoids 2024 workshop : Human movement modeling for Human-Robot interaction at HumanoidsNancy, FranceNovember 2024HALback to text

Doctoral dissertations and habilitation theses

• 19 thesisB.Benjamin Camblor. Exploiting robot motion to improve situation awareness in human-robot collaboration.Université de BordeauxApril 2024HALback to text
• 20 thesisG.Gautier Laisné. Upper-limb force feasible set: theoretical foundations and musculoskeletal model reconstruction.Université de BordeauxDecember 2024HALback to textback to text
• 21 thesisE.Erwann Landais. Teleoperation for the remote expression of technical gestures : application to formulation in chemistry.Université de BordeauxDecember 2024HALback to textback to text

Reports & preprints

• 22 miscV.Virgile Batto, L.Ludovic de Matteis, T.Thomas Flayols, M.Margot Vulliez and N.Nicolas Mansard. CLEO: Closed-Loop kinematics Evolutionary Optimization of bipedal structures.October 2024HALback to text
• 23 miscA.Alexis Boulay, B.Benjamin Camblor, M.Margot Vulliez and D.David Daney. A new unified and adaptive haptic guidance method, an exploratory user study.October 2024HALback to text
• 24 miscE.Elio Jabbour, M.Margot Vulliez, C.Celestin Preault and V.Vincent Padois. A Model Predictive Control Approach To Blending In Shared Control.September 2024HALback to text
• 25 miscG.Guillaume de Mathelin de Papigny, F.Francesco Gassibe and V.Vincent Padois. F-RRT: an Efficient Algorithm for Semi-Constrained Path Planning Problems.September 2024HALback to text
• 26 miscL.Ludovic de Matteis, V.Virgile Batto, J.Justin Carpentier and N.Nicolas Mansard. Optimal Control of Walkers with Parallel Actuation.October 2024HALback to text
• 27 miscA.Antun Skuric, N.Nicolas Torres Alberto, L.Lucas Joseph, V.Vincent Padois and D.David Daney. Online approach to near time-optimal task-space trajectory planning.May 2024HALback to text