Human-Machine Collaboration

Research TOPIC

People

Publications

Projects

Research

We conceive and develop concepts and algorithms of Human-centred Artificial Intelligence (HAI) that create external perception layers for collaborative machines in various industrial real-world situations. We propose HAI-driven collaboration mechanisms that are based on methods and concepts of machine learning and pattern recogni- tion on signals recorded from the human body. We are interested in enabling human-machine partnerships in which the machine can understand and anticipate the human gestures and actions and react accordingly by taking into consideration the human factors. Our scientific hypotheses are validated through a number of tests and experiments conducted on Human-Robot Collaboration, computer-mediated sensori-motor human learning and Digital Musical Instruments.

People

Permanent staff:

  • Sotiris Manitsaris: Researcher, Deputy Director of the Centre for Robotics, sotiris.manitsaris@minesparis.psl.eu
  • Alina Glushkova: Researcher, alina.glushkova@minesparis.psl.eu

PhD students:

  • Brenda Olivas Padilla  (2019-) : Wearable sensing and movement modeling for the monitoring of operators in manufacturing.

Former PhD students:

  • Edgar Hemery. Modeling, recognition of finger gestures and upper-body movements for musical interaction design. 2013-2017, in collaboration with Fabien Moutarde.
  • Florent Taralle. Cifre Sagem. Gestural control for mobile robots. 2013-2016, in collaboration with Alexis Paljic.
  • Eva Coupeté. Recognition of gestures and actions for human-robot collaboration in assembly lines. 2012-2016, in collaboration with Fabien Moutarde.

PUBLICATIONS

  • Zabulis, X., Partarakis, N., Meghini, C., Dubois, A., Manitsaris, S., Hauser, H., … & Cadi, N. (2022). A Representation Protocol for Traditional Crafts. Heritage 2022, 5, 716–741.
  • Olivas-Padilla, B. E., Papanagiotou, D., Senteri, G., Manitsaris, S., & Glushkova, A. (2022). Computational ergonomics for task delegation in Human-Robot Collaboration: spatiotemporal adaptation of the robot to the human through contactless gesture recognition. arXiv preprint arXiv:2203.11007.
  • Carre, A. L., Dubois, A., Partarakis, N., Zabulis, X., Patsiouras, N., Mantinaki, E., … & Manitsaris, S. (2022). Mixed-Reality Demonstration and Training of Glassblowing. Heritage, 5(1), 103-128.
  • Hauser, H., Beisswenger, C., Partarakis, N., Zabulis, X., Adami, I., Zidianakis, E., … Manitsaris, S. (2022). Multimodal Narratives for the Presentation of Silk Heritage in the Museum. Heritage, 5(1), 461-487.
  • Ringas, C., Tasiopoulou, E., Kaplanidi, D., Partarakis, N., Zabulis, X., Zidianakis, E., Manitsaris, S., … & Panesse, L. (2022). Traditional Craft Training and Demonstration in Museums. Heritage, 5(1), 431-459.
  • Manitsaris, S. (2021). Movement-based Human-Machine Collaboration: a Human-centred AI approach (accreditation to supervise research) (Doctoral dissertation, Sorbonne Université).
  • Papanagiotou, D., Senteri, G., & Manitsaris, S. (2021). Egocentric Gesture Recognition Using 3D Convolutional Neural Networks for the Spatiotemporal Adaptation of Collaborative Robots. Frontiers in Neurorobotics, 15, 703545-703545.
  • Dimitropoulos, K., Daras, P., Manitsaris, S., Fol Leymarie, F., & Calinon, S. (2021). Editorial: Artificial Intelligence and Human Movement in Industries and Creation. Frontiers in AI and Robotics. 8: 712521. doi: 10.3389/frobt.
  • Olivas-Padilla, B. E., Manitsaris, S., Menychtas, D., & Glushkova, A. (2021). Stochastic-Biomechanic Modeling and Recognition of Human Movement Primitives, in Industry, Using Wearables. Sensors, 21(7), 2497.
  • Menychtas, D., Glushkova, A., & Manitsaris, S. (2020). Analyzing the kinematic and kinetic contributions of the human upper body’s joints for ergonomics assessment. Journal of Ambient Intelligence and Humanized Computing, 11(12), 6093-6105.
  • Olivas-Padilla, B. E., Menychtas, D., Glushkova, A., & Manitsaris, S. (2020, October). Hidden Markov Modelling And Recognition Of Euler-Based Motion Patterns For Automatically Detecting Risks Factors From The European Assembly Worksheet. In 2020 IEEE International Conference on Image Processing (ICIP) (pp. 3259-3263). IEEE.
  • Manitsaris, S., Senteri, G., Makrygiannis, D., & Glushkova, A. (2020). Human movement representation on multivariate time series for recognition of professional gestures and forecasting their trajectories. Frontiers in Robotics and AI, 7, 80.
  • Padilla, B. E. O., Glushkova, A., & Manitsaris, S. (2020). Motion analysis for identification of overused body segments: the packaging task in industry 4.0. Human Computer Interaction and Emerging Technologies: Adjunct Proceedings from, 349.

ONGOING EU projects

  • Collaborate – H2020
  • Mingei – H2020
  • Digitraining – Creative Europe