[TMP-038] Educational module Human-Interactive Robot Learning (HIRL)

Contact person: Mohamed Chetouani (mohamed.chetouani@sorbonne-universite.fr) 

Internal Partners:

1. ISIR, Sorbonne University, Mohamed Chetouani, Silvia Tulli
2. Vrije Universiteit Amsterdam, Kim Baraka  

Human-Interactive Robot Learning (HIRL) is an area of robotics that focuses on developing robots that can learn from and interact with humans. This educational module aims to cover the basic principles and techniques of Human-Interactive Robot Learning. This interdisciplinary module will encourage graduate students (Master/PhD level) to connect different bodies of knowledge within the broad field of Artificial Intelligence, with insights from Robotics, Machine Learning, Human Modelling, and Design and Ethics. The module is meant for Master’s and PhD students in STEM, such as Computer Science, Artificial Intelligence, and Cognitive Science. This work will extend the tutorial presented in the context of the International Conference on Algorithms, Computing, and Artificial Intelligence (ACAI 2021) and will be shared with the Artificial Intelligence Doctoral Academy (AIDA). Moreover, the proposed lectures and assignments will be used as teaching material at Sorbonne University, and Vrije Universiteit Amsterdam. We plan to design a collection of approximately 12 1.5-hour lectures, 5 assignments, and a list of recommended readings, organized along relevant topics surrounding HIRL. Each lecture will include an algorithmic part and a practical example of how to integrate such an algorithm into an interactive system. The assignments will encompass the replication of existing algorithms with the possibility for the student to develop their own alternative solutions. Proposed module contents (each lecture approx. 1.5 hour): (1) Interactive Machine Learning vs Machine Learning – 1 lecture, (2) Interactive Machine Learning vs Interactive Robot Learning (Embodied vs non-embodied agents) – 1 lecture, (3) Fundamentals of Reinforcement Learning – 2 lectures, (4) Learning strategies: observation, demonstration, instruction, or feedback- Imitation Learning, Learning from Demonstration – 2 lectures- Learning from Human Feedback: evaluative, descriptive, imperative, contrastive examples – 3 lectures, (5) Evaluation metrics and benchmarks – 1 lecture, (6) Application scenarios: hands-on session – 1 lecture, and (7) Design and ethical considerations – 1 lecture.