arXiv 2411.057182024-11-08cs.ROcs.AIcs.LG

机器人空气曲棍球挑战赛回顾：基准测试鲁棒、可靠且安全的真实世界机器人学习技术

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

Intelligent Autonomous Systems, TU Darmstadt（达姆施塔特工业大学智能自主系统研究所）；German Research Center for AI (DFKI)（德国人工智能研究中心）；Huawei German research center（华为德国研究中心）；Huawei Noah’s Ark Lab（华为诺亚方舟实验室）；University College London（伦敦大学学院）；Idiap Research Institute（伊迪亚普研究所）；EPFL（洛桑联邦理工学院）；University of Luxembourg（卢森堡大学）；Karlsruher Institut für Technologie（卡尔斯鲁厄理工学院）；Universita di Bologna（博洛尼亚大学）；Politecnico di Milano（米兰理工大学）；Centre for Cognitive Science（认知科学中心）

Puze Liu, Jonas Günster, Niklas Funk, Simon Gröger, Dong Chen, Haitham Bou-Ammar, Julius Jankowski, Ante Marić, Sylvain Calinon, Andrej Orsula, Miguel Olivares-… 展开作者

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, Jan Peters

备注：: Accept at NeurIPS 2024 Dataset and Benchmark Track

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AI总结：

本文回顾了NeurIPS 2023机器人空气曲棍球挑战赛，该竞赛通过动态环境下的真实机器人任务，评估了结合先验知识的学习方法在应对仿真到现实差距、安全等挑战中的优势，并指出了被忽视的因素。

摘要：

机器学习方法在许多应用领域产生了突破性影响，但它们在真实机器人平台上的应用仍然有限。尽管将机器学习技术与机器人技术相结合面临诸多挑战，机器人学习仍然是增强机器人能力最有前景的方向之一。当在真实机器人上部署基于学习的方法时，需要额外努力来应对各种真实世界因素带来的挑战。为了研究影响真实世界部署的关键因素并鼓励不同研究者提出原创解决方案，我们在NeurIPS 2023会议上组织了机器人空气曲棍球挑战赛。我们选择空气曲棍球任务作为基准，涵盖低级机器人问题和高级战术。与其他以机器学习为中心的基准不同，参与者需要解决机器人学中的实际挑战，例如仿真到现实的差距、低级控制问题、安全问题、实时要求以及真实世界数据的有限可用性。此外，我们关注动态环境，去除了其他真实世界基准中常见的准静态运动假设。比赛结果表明，在真实世界部署具有挑战性时，将基于学习的方法与先验知识相结合的解决方案优于仅依赖数据的解决方案。我们的消融研究揭示了在构建基于学习的解决方案时可能被忽视的真实世界因素。表现最佳的智能体在真实世界空气曲棍球部署中的成功为未来的竞赛和后续研究方向奠定了基础。

英文摘要：

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.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。