Abstract
The development of robust learning-based control algorithms for unstable systems requires high-quality, real-world data, yet access to specialized robotic hardware remains a significant barrier for many researchers. This paper introduces a comprehensive dynamics dataset for the Mini Wheelbot, an open-source, quasi-symmetric balancing reaction wheel unicycle. The dataset provides 1 kHz synchronized data encompassing all onboard sensor readings, state estimates, ground-truth poses from a motion capture system, and third-person video logs. To ensure data diversity, we include experiments across multiple hardware instances and surfaces using various control paradigms, including pseudo-random binary excitation, nonlinear model predictive control, and reinforcement learning agents. We include several example applications in dynamics model learning, state estimation, and time-series classification to illustrate common robotics algorithms that can be benchmarked on our dataset.
Abstract (translated)
对于不稳定系统的基于学习的控制算法的发展需要高质量的真实世界数据，然而获取专门的机器人硬件仍然是许多研究人员面临的重大障碍。本文介绍了一个全面的动力学数据集，用于开源准对称平衡反应轮独轮车——Mini Wheelbot。该数据集提供了1 kHz同步数据，涵盖了所有机载传感器读数、状态估计以及由运动捕捉系统提供的真实姿态和第三人称视频日志。 为了确保数据的多样性，我们包含了在不同硬件实例和表面上使用各种控制范例进行的不同实验，包括伪随机二进制激励、非线性模型预测控制和强化学习代理。我们还提供了几个示例应用，涵盖了动力学模型学习、状态估计和时间序列分类等领域，以展示可以在此数据集上进行基准测试的常见机器人算法。
URL
https://arxiv.org/abs/2601.11394
