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.

翻译：为不稳定系统开发鲁棒的学习型控制算法需要高质量的真实世界数据，然而获取专用机器人硬件仍然是许多研究者的主要障碍。本文为微型轮式机器人——一种开源、准对称的平衡反作用轮独轮车——引入了一个全面的动力学数据集。该数据集提供1 kHz的同步数据，涵盖所有板载传感器读数、状态估计、来自运动捕捉系统的真实位姿以及第三人称视频日志。为确保数据多样性，我们纳入了使用多种控制范式（包括伪随机二进制激励、非线性模型预测控制和强化学习智能体）在多个硬件实例和不同表面上进行的实验。我们提供了在动力学模型学习、状态估计和时间序列分类方面的若干示例应用，以说明可在本数据集上进行基准测试的常见机器人学算法。