TR2023-111

Athletic Intelligence Olympics challenge with Model-Based Reinforcement Learning

    •  Dalla Libera, A., Turcato, N., Giacomuzzo, G., Carli, R., Romeres, D., "Athletic Intelligence Olympics challenge with Model-Based Reinforcement Learning", International Joint Conference on Artificial Intelligence, August 2023.
      BibTeX TR2023-111 PDF
      • @inproceedings{DallaLibera2023aug,
      • author = {Dalla Libera, Alberto and Turcato, Niccolò and Giacomuzzo, Giulio and Carli, Ruggero and Romeres, Diego},
      • title = {Athletic Intelligence Olympics challenge with Model-Based Reinforcement Learning},
      • booktitle = {International Joint Conference on Artificial Intelligence},
      • year = 2023,
      • month = aug,
      • url = {https://www.merl.com/publications/TR2023-111}
      • }
  • MERL Contact:
  • Research Area:

    Robotics

Abstract:

In this report, we describe the solution we propose for the AI Olympics competition held at IJCAI 2023. Our solution is based on a recent Model-Based Reinforcement Learning algorithm named MC-PILCO. Besides briefly reviewing the algorithm, we discuss the most critical aspects of the MC-PILCO implementation in the environments at hand.

 

  • Related News & Events

    •  AWARD    Joint University of Padua-MERL team wins Challenge 'AI Olympics With RealAIGym'
      Date: August 25, 2023
      Awarded to: Alberto Dalla Libera, Niccolo' Turcato, Giulio Giacomuzzo, Ruggero Carli, Diego Romeres
      MERL Contact: Diego Romeres
      Research Areas: Artificial Intelligence, Machine Learning, Robotics
      Brief
      • A joint team consisting of members of University of Padua and MERL ranked 1st in the IJCAI2023 Challenge "Al Olympics With RealAlGym: Is Al Ready for Athletic Intelligence in the Real World?". The team was composed by MERL researcher Diego Romeres and a team from University Padua (UniPD) consisting of Alberto Dalla Libera, Ph.D., Ph.D. Candidates: Niccolò Turcato, Giulio Giacomuzzo and Prof. Ruggero Carli from University of Padua.

        The International Joint Conference on Artificial Intelligence (IJCAI) is a premier gathering for AI researchers and organizes several competitions. This year the competition CC7 "AI Olympics With RealAIGym: Is AI Ready for Athletic Intelligence in the Real World?" consisted of two stages: simulation and real-robot experiments on two under-actuated robotic systems. The two robotics systems were treated as separate tracks and one final winner was selected for each track based on specific performance criteria in the control tasks.

        The UniPD-MERL team competed and won in both tracks. The team's system made strong use of a Model-based Reinforcement Learning algorithm called (MC-PILCO) that we recently published in the journal IEEE Transaction on Robotics.
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