Publication
Reinforcement Learning for Robust Athletic Intelligence: Lessons from the 2nd 'AI Olympics with RealAIGym' Competition
Felix Wiebe; Niccolò Turcato; Alberto Dalla Libera; Jean Seong Bjorn Choe; BumKyu Choi; Tim Lukas Faust; Habib Maraqten; Erfan Aghadavoodi; Marco Calì; Alberto Sinigaglia; Giulio Giacomuzzo; Diego Romeres; Jong-Kook Kim; Gian Antonio Susto; Shubham Vyas; Dennis Mronga; Boris Belousov; Jan Peters; Frank Kirchner; Shivesh Kumar
In: Computing Research Repository eprint Journal (CoRR), Vol. abs/2503.15290, Pages 1-8, arXiv, 2025.
Abstract
In the field of robotics many different approaches
ranging from classical planning over optimal control to re-
inforcement learning (RL) are developed and borrowed from
other fields to achieve reliable control in diverse tasks. In
order to get a clear understanding of their individual strengths
and weaknesses and their applicability in real world robotic
scenarios is it important to benchmark and compare their
performances not only in a simulation but also on real hard-
ware. The ’2nd AI Olympics with RealAIGym’ competition was
held at the IROS 2024 conference to contribute to this cause
and evaluate different controllers according to their ability to
solve a dynamic control problem on an underactuated double
pendulum system (Fig. 1) with chaotic dynamics. This paper
describes the four different RL methods submitted by the
participating teams, presents their performance in the swing-
up task on a real double pendulum, measured against various
criteria, and discusses their transferability from simulation to
real hardware and their robustness to external disturbances.
