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