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.
Keywords
- Robotics: ROB: Learning in robotics
- Robotics: ROB: Motion and path planning
- Machine Learning: ML: Deep reinforcement learning
- Robotics: ROB: Behavior and control
