A Retrospective on the Robot Air Hockey Challenge: Benchmarking Robust, Reliable, and Safe Learning Techniques for Real-world Robotics

Puze Liu, Jonas Günster, Niklas Funk, Simon Gröger, Dong Chen, Haitham Bou Ammar, Julius Jankowski, Ante Marić, Sylvain Calinon, Andrej Orsula, Miguel Olivares-Mendez, Hongyi Zhou, Rudolf Lioutikov, Gerhard Neumann, Amarildo Likmeta, Amirhossein Zhalehmehrabi, Thomas Bonenfant, Marcello Restelli, Davide Tateo, Ziyuan Liu et al. (1 additional authors not shown)

Keywords: robotics, reinforcement learning, imitation learinng, planning, challenge

TL;DR: A Retrospective of the NeurIPS Robot Air Hockey Challenge 2023

Abstract:

Machine learning methods have a groundbreaking impact in many application domains, but their application on real robotic platforms is still limited.
Despite the many challenges associated with combining machine learning technology with robotics, robot learning remains one of the most promising directions for enhancing the capabilities of robots.
When deploying learning-based approaches on real robots, extra effort is required to address the challenges posed by various real-world factors. To investigate the key factors influencing real-world deployment and to encourage original solutions from different researchers, we organized the Robot Air Hockey Challenge at the NeurIPS 2023 conference.
We selected the air hockey task as a benchmark, encompassing low-level robotics problems and high-level tactics. Different from other machine learning-centric benchmarks, participants need to tackle practical challenges in robotics, such as the sim-to-real gap, low-level control issues, safety problems, real-time requirements, and the limited availability of real-world data. Furthermore, we focus on a dynamic environment, removing the typical assumption of quasi-static motions of other real-world benchmarks.
The competition's results show that solutions combining learning-based approaches with prior knowledge outperform those relying solely on data when real-world deployment is challenging.
Our ablation study reveals which real-world factors may be overlooked when building a learning-based solution.
The successful real-world air hockey deployment of best-performing agents sets the foundation for future competitions and follow-up research directions.

NeurIPS 23 Competition Analysis Paper: NeurIPS23 Competition Analysis Paper

NeurIPS 23 Competition Name For Competition Analysis Papers Only: The Robot Air Hockey Challenge: Robust, Reliable, and Safe Learning Techniques for Real-world Robotics

Submission Number: 1159