The Mini Wheelbot is a balancing, reaction wheel unicycle robot designed as a testbed for learning-based control. It is an unstable system with highly nonlinear yaw dynamics, non-holonomic driving, and discrete contact switches in a small, powerful, and rugged form factor. The Mini Wheelbot can use its wheels to stand up from any initial orientation - enabling automatic environment resets in repetitive experiments and even challenging half flips. We illustrate the effectiveness of the Mini Wheelbot as a testbed by implementing two popular learning-based control algorithms. First, we showcase Bayesian optimization for tuning the balancing controller. Second, we use imitation learning from an expert nonlinear MPC that uses gyroscopic effects to reorient the robot and can track higher-level velocity and orientation commands. The latter allows the robot to drive around based on user commands - for the first time in this class of robots. The Mini Wheelbot is not only compelling for testing learning-based control algorithms, but it is also just fun to work with, as demonstrated in the video of our experiments.

翻译：微型轮式机器人是一种平衡式反作用轮独轮机器人，专为基于学习的控制研究而设计。该系统具有高度不稳定性，其偏航动力学呈现强非线性特征，同时具备非完整约束驱动特性以及离散接触切换机制，并以紧凑、高功率且坚固的形态实现。微型轮式机器人能够利用其车轮从任意初始姿态自主起立——这一特性使得在重复性实验中可实现环境自动重置，甚至能完成极具挑战性的半周翻转动作。我们通过部署两种主流的基于学习的控制算法，验证了微型轮式机器人作为测试平台的有效性。首先，我们展示了贝叶斯优化在平衡控制器调参中的应用。其次，我们采用基于专家非线性模型预测控制器的模仿学习方法，该控制器利用陀螺效应实现机器人重定向，并能跟踪高层速度与姿态指令。后一种方法首次在该类机器人中实现了基于用户指令的自主驾驶功能。微型轮式机器人不仅是测试基于学习的控制算法的理想平台，其操作过程也极具趣味性，这在我们实验视频中得到了充分展现。