As a follow-up to the Extender project, the participants met again to discuss their respective progress.

Objective

This second meeting had two main objectives. The first was to gather feedback on the initial alpha tests conducted at clinics in Nantes and Clermont-Ferrand. The second was for the research laboratories to present their new technological building blocks and their progress on the project.

For the Auctus team, which expanded in May with the arrival of Julien Bernard and Nicolas Delbrel, three key building blocks were highlighted.

The PhD work of Alexis was presented at Eurohaptics 2026. It contributes to haptic guidance in teleoperation with guidelines to properly define and select a haptic guidance model and to evaluate the human-guidance interaction.

Abstract

Haptic guidance in teleoperation enhances operator performance through force feedback. This paper presents guidelines to select the most appropriate model considering the task, the environment and the operator. We define a unified \added{formulation} expressing most common models (spring-damper, potential field, and guiding tube) as variations of a stiffness-damping system with model-specific guiding functions. We conducted a user study comparing the three classical models across six scenarios with varying environmental conditions in a vertical farming task. Results show no universally superior model: spring-damper excels in cluttered environments, potential field in free spaces (but it shows risks near obstacles), and guiding tube offers a balanced compromise. We propose novel objective metrics to evaluate the interaction, and show that guiding force magnitude correlates with comfort and trust scores. These findings provide practical model selection guidelines through environmental characteristics and real-time evaluation metrics.

During the “Assistive and Rehabilitation Robotics” conference of the GDR Robotique, Esteban COSSERAT presented his work on how to ensure the safety of teleoperation of a robotic arm mounted on the wheelchair of a person with a disability. Such an application raises numerous challenges, such as minimizing risks to the user, the robot, and the surrounding environement, particularly because the user who controls the robotic arm is located within its workspace.

The PhD work of Elio was presented at ICRA 2026. It contributes to blending in shared control with a Model Predictive Control approach that enforces safety and improves the assistance quality.

Abstract

Shared control methods distribute control between human operators and robots in demanding tasks, enabling collaboration that leverages their respective strengths and expertise. Sharing the task typically involves blending algorithms that combine human control inputs to (pre)planed assistance trajectories. Conventional blending techniques, such as Linear Blending, compute a combined output but neither guarantee feasibility of this shared motion, nor ensure compliance with safety or task-related constraints. This paper proposes to tackle feasibility and safety by formulating the blending strategy as the solution of a constrained optimal control problem, that enforces environment limits, task requirements, and physical capabilities. A Model Predictive Control approach is used to solve the optimization problem and anticipate constraints by predicting the robot motion over a receding time horizon. We evaluate this approach in simulated and real-world pick-and-place teleoperation experiments. The experimental study compares the Model Predictive Control approach to Linear Blending and full Teleoperation. The results show that the new framework offers significant improvements, as it provides a safer, more accurate, and repeatable response.

Congratulations to Pierre for this contribution advancing fast and clinically applicable upper-limb musculoskeletal modeling.

Abstract

This paper addresses the challenge of estimating personalized muscle forces through musculoskeletal modeling, which is valuable for assessing patient status and monitoring clinical progress. Upper-limb applications have been limited due to system complexity and the long computation times of existing calibration methods. We propose a fast (<5 min) calibration method for upper-limb models, calibrating maximal isometric force and optimal muscle length for 38 muscles across 10 degrees of freedom by matching muscle-generated moments with dynamically consistent joint moments. The method leverages experimental data including bony landmark trajectories from markerless motion capture, external forces, and electromyography (EMG). During hand-cycling, the calibrated model reduced EMG tracking error compared to the uncalibrated model (5.58±0.92% vs. 6.30±1.28%), and reliance on non-physiological residual moments was also lowered (12.68 vs. 23.61% of peak moment). This approach provides a fast and reliable framework for upper-limb musculoskeletal calibration, facilitating more accurate and clinically applicable muscle force estimation.