Reinforcement Learning for Athletic Intelligence: Lessons from the 1st “AI Olympics with RealAIGym” Competition

Reinforcement Learning for Athletic Intelligence: Lessons from the 1st “AI Olympics with RealAIGym” Competition

Felix Wiebe, Niccolò Turcato, Alberto Dalla Libera, Chi Zhang, Theo Vincent, Shubham Vyas, Giulio Giacomuzzo, Ruggero Carli, Diego Romeres, Akhil Sathuluri, Markus Zimmermann, Boris Belousov, Jan Peters, Frank Kirchner, Shivesh Kumar

Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence
Demo Track. Pages 8833-8837. https://doi.org/10.24963/ijcai.2024/1043

As artificial intelligence gains new capabilities, it becomes important to evaluate it on real-world tasks. In particular, the fields of robotics and reinforcement learning (RL) are lacking in standardized benchmarking tasks on real hardware. To facilitate reproducibility and stimulate algorithmic advancements, we held an AI Olympics competition at IJCAI 2023 conference based on the double pendulum system in the RealAIGym project where the participants were asked to develop a controller for the swing up and stabilization task. This paper presents the methods and results from the top participating teams and provides insights into the real-world performance of RL algorithms with respect to a baseline time-varying LQR controller.
Keywords:
Robotics: ROB: Learning in robotics
Robotics: ROB: Motion and path planning
Machine Learning: ML: Deep reinforcement learning
Robotics: ROB: Behavior and control