Master Internship (6 months, March-August 2024):
The effect of Joint Attention and Decision-Making in Playing Hex-game with the robot iCub
Joint attention (JA) is a fundamental skill for human social cognition , allowing individuals to focus on things together with others. Through this capacity, humans can share experiences about the world, coordinate thoughts and behaviors, and successfully cooperate with others. Reduced JA is associated with deficits in social cognition, which is observed in psychological conditions such as autism spectrum disorders (ASD) .
By continuing a previous study where the TOP-JAM model is proposed to track JA in human-robot interaction , the objective of this project is to develop an experimental prototype for JA skill training, consisting in playing Hex-game against the robot iCub . Through the prototype developed, the following hypothesis are going to be evaluated:
i) Shorter decision-making time is obtained by engaging in JA with iCub from gaze cueing.
ii) The game situation and embodiment can help training JA skills in ASD persons.
A face-to-face interaction scenario is planned, consisting of playing Hex-game with iCub through a touch screen. In Hex-Game, the first-mover in the best case is a must-win, and no draws occur. The robot iCub will produce behavior cues announcing its next move intention and then act by emulating touching the screen with its finger. Participants will play the game in two conditions: with and without the robot (i.e. against an AI algorithm). Participants’ behavior will be registered from RGBD cameras and an eye-tracker system Tobii ProGlasses.
The experiment version of Hex-Game has been implemented in Python 3 programming language. The program ensures communication with the robot for synchronizing the game’s screen view with the robot’s emulated decision-making and behavior. Hence, the work to be done consists in:
- Refining the robot’s behavior in the game by using available controllers.
- Integrating the framework for JA estimation to the prototype by fusing data from sensors, capturing the human’s and the robot’s state.
- Ensuring synchronized data acquisition in the experiment.
Conducting a pilot test to evaluate the prototype in a reduced sample of subjects.
- Analyzing results.
This research is contextualized in an international collaboration project between the Kyushu Institute of Technology (Kyutech) from Kitakyushu, Japan, and the University of Lorraine from Nancy, France. The research institutions involved are: the lab Human and Social Intelligence Systems (Shibata’s team) and the lab LORIA-CNRS (Neurorhythms team).
The internship will be developed in the Neurorhythms team at LORIA-CNRS lab between March and August 2024 (6 months). A short stay is planned (to be confirmed) to the Human and Social Intelligence Systems lab (Shibata’s team).
- Deep Interest in human-robot interaction, embodiment, cognitive sciences and bio-inspired modeling (dynamical systems theory).
- Programming skills in Python language (skills in C++ would be a plus).
- Notions of classical geometric modeling and behavior regulation in robotics (you understand what a direct / inverse geometric and kinematic model is).
- Knowledge in robotics middle-ware software (e.g. YARP, ROS).
- You can communicate and do presentations in English and French.
Application (Deadline: 15 of December 2023)
If you are interested in this position please send a motivation letter, CV, and the most recent transcript of your academic records to Hendry Ferreira Chame at the e-mail address: firstname.lastname@example.org
 Siposova, B., & Carpenter, M. (2019). A new look at joint attention and common knowledge. Cognition, 189, 260-274.
 Hyman, S. L., Levy, S. E., Myers, S. M., Kuo, D. Z., Apkon, S., Davidson, L. F., … & Bridgemohan, C. (2020). Identification, evaluation, and management of children with autism spectrum disorder. Pediatrics, 145(1).
 Chame, H. F., Clodic, A. & Alami, R. TOP-JAM: A bio-inspired topology-based model of joint attention for human-robot interaction. 2023 IEEE International Conference on Robotics and Automation (ICRA), London, United Kingdom, 2023, pp. 7621-7627, doi: 10.1109/ICRA48891.2023.10160488.
 Metta, G., Natale, L., Nori, F., Sandini, G., Vernon, D., Fadiga, L., … & Montesano, L. (2010). The iCub humanoid robot: An open-systems platform for research in cognitive development. Neural networks, 23(8-9), 1125-1134.