Developing a human-robot collaborative workplace is the solution to perform faster and more efficient tasks by merging human cognition, awareness, and consciousness with the robot’s power generation, capacity, and precision. In this paper, we address the problem of manipulating linear deformable objects such as cables, ropes, or textiles in a collaborative setup.The proposed method is based on a real-time model-based control algorithm used to position a given point belonging to the object, which is grasped by a human and a robot at its endpoints. The basis of this method lies in (i) the theory of catenaries for modeling the object’s deformation in real-time (ii) the formulation of an interaction matrix representing the robot controller gradient to reach the target position. The experimental results show that the proposed method is reactive to human motion during manipulation and able to reach the desired position accurately.
Racha Ghaddar, A. Koessler, Mourad Benoussaad et al.· 2026 IEEE/ASME International...· 0 citations
The problem of shaping soft objects is widespread in industrial, medical, and household settings. Hence, robotic Deformable Object Manipulation (DOM) is a field of research that has recently emerged to improve robotic systems’ ability to handle such objects. Indeed, human-robot collaboration is also relevant to applications featuring soft objects, since the decision-making and dexterity of a human operator are currently beyond reach.Our aim is to assess the feasibility of using fast finite element inverse simulation in collaborative shaping tasks. To this end, we propose a computationally efficient method for controlling the shape of an object grasped at both ends. In our experimental setup, a leader robot moves freely along unplanned trajectories, while a controlled robot maintains the desired shape despite these perturbations. We reach an update frequency of 20 Hz for the inverse simulation, with average steady-state shape errors below 10 mm in most cases. Those satisfying results allow us to envision our next milestone: the deployment of the inverse simulation in real human-robot shaping tasks.
A. Koessler, T. Raharijaona, H. Courtecuisse· 2026 IEEE/ASME International...· 0 citations