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
Abstract
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.
BibTeX
@inproceedings{ijcai2024p1043,
title = {Reinforcement Learning for Athletic Intelligence: Lessons from the 1st “AI Olympics with RealAIGym” Competition},
author = {Wiebe, Felix and Turcato, Niccolò and Dalla Libera, Alberto and Zhang, Chi and Vincent, Theo and Vyas, Shubham and Giacomuzzo, Giulio and Carli, Ruggero and Romeres, Diego and Sathuluri, Akhil and Zimmermann, Markus and Belousov, Boris and Peters, Jan and Kirchner, Frank and Kumar, Shivesh},
booktitle = {Proceedings of the Thirty-Third International Joint Conference on
Artificial Intelligence, {IJCAI-24}},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
editor = {Kate Larson},
pages = {8833--8837},
year = {2024},
month = {8},
note = {Demo Track},
doi = {10.24963/ijcai.2024/1043},
url = {https://doi.org/10.24963/ijcai.2024/1043},
}