Cognitive Robots for Flexible Agro Food Technology (FlexCRAFT)

FlexCRAFT Website

Project type

NWO Perspectief; 2019-2025


Food production must be as hygienic, efficient and sustainable as possible. Furthermore, fewer people are willing to do tedious and heavy work in warm greenhouses or in refrigerated rooms where chicken products are processed, for example. Robots can provide a solution to this problem if they can deal with the considerable variations in shape, size and hardness of different food products. This is still challenging. The programme FlexCRAFT will develop new robot technology for such purposes as the automatic harvesting of tomatoes, for example. The robotics developed must also help with the processing of foodstuffs. Examples of this include the processing and packaging of chicken products, but also neatly packaging bags of crisps and packets of biscuits in boxes of varying sizes.

The Netherlands is the second biggest exporter of agro-food products worldwide and the third biggest supplier of technology for the agro-food sector. This programme will contribute to strengthening the competitive position of the Netherlands in these sectors.

COR project members

ir. Tomás Coleman, ir. Padmaja Kulkarni, ir. Rodrigo J. Pérez-Dattari, Dr. Cosimo Della Santina, Dr.-Ing. Jens Kober, prof.dr. Robert Babuška

Project consortium

3DUniversum, ABB, AgriFoodTech Platform, Aris BV, BluePrint Automation, Cellar Land, Cerescon, Delft University of Technology, Demcon, Eindhoven University of Technology, Festo, GMV, Houdijk Holland, Marel Stork Poultry Processing, Maxon Motor, Priva, Protonic Holland, Rijk Zwaan, University of Amsterdam, University of Twente, Wageningen University & Research

Publications with videos

Rodrigo Pérez-Dattari, Cosimo Della Santina, and Jens Kober. Deep Metric Imitation Learning for Stable Motion Primitives. arXiv:2310.12831 [cs.RO], 2023. [bibtex] [pdf] [doi] [code] [video] bronze open access

Rodrigo Pérez-Dattari and Jens Kober. Stable Motion Primitives via Imitation and Contrastive Learning. IEEE Transactions on Robotics, 39(5):3909–3928, 2023. [bibtex] [pdf] [doi] [code] [video] green open access

Rodrigo Pérez-Dattari, Carlos E. Celemin, Giovanni Franzese, Javier Ruiz-del-Solar, and Jens Kober. Interactive Learning of Temporal Features for Control: Shaping Policies and State Representations From Human Feedback. IEEE Robotics & Automation Magazine, 27(2):46–54, 2020. [bibtex] [pdf] [doi] [code] [video] green open access

Publications without videos

Tomás Coleman, Robert Babuška, Jens Kober, and Cosimo Della Santina. An Empirical Investigation on Variational Autoencoder-Based Dynamic Modeling of Deformable Objects from RGB Data. In 32nd Mediterranean Conference on Control and Automation (MED), pp. 921-928, 2024. [bibtex] [pdf] [doi] green open access

Eveline Drijver, Rodrigo Pérez-Dattari, Jens Kober, Cosimo Della Santina, and Zlatan Ajanović. Robotic Packaging Optimization with Reinforcement Learning. In IEEE 19th International Conference on Automation Science and Engineering (CASE), pp. 1-7, 2023. [bibtex] [pdf] [doi] green open access

Rodrigo Pérez-Dattari, Bruno Brito, Oscar de Groot, Jens Kober, and Javier Alonso-Mora. Visually-Guided Motion Planning for Autonomous Driving from Interactive Demonstrations. Engineering Applications of Artificial Intelligence, 116:105277, 2022. [bibtex] [url] [doi] [code] [video] gold open access

Padmaja Kulkarni, Jens Kober, Robert Babuška, and Cosimo Della Santina. Learning Assembly Tasks in a Few Minutes by Combining Impedance Control and Residual Recurrent Reinforcement Learning. Advanced Intelligent Systems, 4(1):2100095, 2022. [bibtex] [file] [doi] [video] gold open access

Carlos Celemin, Rodrigo Pérez-Dattari, Eugenio Chisari, Giovanni Franzese, Leandro de Souza Rosa, Ravi Prakash, Zlatan Ajanović, Marta Ferraz, Abhinav Valada, and Jens Kober. Interactive Imitation Learning in Robotics: A Survey. Foundations and Trends® in Robotics, 10(1–2):1–197, 2022. [bibtex] [pdf] [url] [doi] green open access

Padmaja Kulkarni, Robert Babuška, and Jens Kober. Tactile-based Self-supervised Pose Estimation for Robust Grasping. In 17th International Symposium on Experimental Robotics (ISER) (Bruno Siciliano, Cecilia Laschi, Oussama Khatib, eds.), Springer International Publishing, Cham, pp. 277–284, 2021. [bibtex] [pdf] [doi] green open access