AUCTUS is an Inria Project team in collaboration with ENSC. The general objective of the team is to design robotic assistance systems or collaborative robots for Humans at work, in particular in the industrial sector.

The increase of the physical and cognitive capacities of the Homo Faber through the development of tools knows a new golden age by the advent of the collaborative robotics coupled with the artificial intelligence. Man is able to share with a machine his movement, his motor intelligence, but also his decisions. The challenge is then to design the machine part of the cybernetic couple for the successful realization of a task, while preserving the man in his physical and cognitive integrity and in his capacity of adaptation and decision.

The robotics community still tends to separate the cognitive (HRI) and physical (pHRI) aspects of human/robot interaction. One of the main challenges is to characterize the task as well as mechanical, physiological and cognitive capacities of humans in the form of physical constraints or objectives for the design of cobotized workstations. This design is understood in a large sense: the choice of the robot’s architecture (cobot, exoskeleton, etc.), the dimensional design (human/robot workspace, trajectory calculation, etc.), the coupling mode (comanipulation, teleoperation, etc.) and control. The approach then requires the contributions of the human and social sciences to be considered in the same way as those of exact sciences. The topics considered are broad, ranging from cognitive sciences, ergonomics, human factors, biomechanics and robotics.

Scientific Axes

  • Analysis and modeling of behavior
    • Links between Human Sciences and Artificial Intelligence
    • Set analysis of postures, gestures and human movements
  • Operator / robot coupling
    • Optimizing the performance of an operator / robot couple
    • Mediation of perceptions of an operator / robot couple
  • Design of collaborative robots and robotic assistance systems
    • Architectural design
    • Control design
  • Methodological support: experiments and technological developments
    • Innovative sensors
    • Experiments

Latest News

The Auctus team is delighted to receive the visit of Philip Long on November 6 and 7. Dr. Philip Long is a Lecturer in Robotics & Automation at Atlantic Technological University, Galway (ATU), Ireland and currently PI of the SFI Robomate project. His research interests include human-robot collaboration, sensor-based control of robot manipulators, cable-driven parallel robots and flexible manufacturing. He has extensive experience in technology transfer projects in robotics, having worked in both industry and academia for over 15 years. [Read More]
Abstract This thesis is based on vision of the future where robotics and industry are centred around humans, emphasising collaboration between humans and robots rather than mere automation. In this collaborative future, robots serve as active assistants, coexisting closely with humans and engaging in physical interactions to execute tasks. Such symbiotic systems leverage the unique abilities of both humans and robots, enhancing efficiency and prioritising human safety and well-being through personalised robotic assistance. [Read More]
The Auctus team is delighted to receive the visit of Ludovic Righetti on October 23 and 24. Ludovic Righetti is an associate professor at the Tandon School of Engineering of New York University. Currently on sabbatical leave, he is a visiting researcher at the LAAS-CNRS in Toulouse. He holds an Engineering Diploma in Computer Science and a Doctorate in Science from the Ecole Polytechnique Fédérale de Lausanne. He was previously a postdoctoral fellow at the University of Southern California and a group leader at the Max-Planck Institute for Intelligent Systems. [Read More]
Abstract Collaborative robotics involves the transformation of industrial robots to function in shared workspaces alongside humans, resulting in more compact and manageable robotic systems. This evolution necessitates a reevaluation of the requirements for robot controllers. Safety remains paramount, but robots must also adapt to dynamic contexts where objects are in constant motion, and conditions are ever-changing as people carry out their tasks. To address these challenges, this work proposes using Model Predictive Control (MPC) to empower the robot to dynamically adjust its operations in a responsive manner. [Read More]