小型自平衡机器人实现学习控制，可自动翻正并按指令移动。
The Mini Wheelbot: A Testbed for Learning-based Balancing, Flips, and Articulated Driving
- 通过强化学习与模仿学习实现动态平衡与翻正控制
- 支持从任意姿态自动站起，完成半翻等复杂动作
- 适合研究学习型控制算法，兼具实验价值与趣味性
Mini Wheelbot 是一款用于学习型控制的自平衡反应轮独轮机器人，具有高度非线性的偏航动力学、非完整驱动特性以及小体积、高功率、坚固耐用的设计。该机器人能利用车轮从任意初始姿态自行站起，支持重复实验的自动环境重置，甚至完成挑战性的半翻动作。我们通过实现两种主流学习型控制算法验证其有效性：首先使用贝叶斯优化调参平衡控制器；其次采用模仿学习，从专家级非线性模型预测控制（MPC）中学习，利用陀螺效应重定向机器人，并能跟踪更高层次的速度与方向指令。该方法首次使此类机器人能够根据用户命令自主行驶。Mini Wheelbot 不仅是学习型控制算法的理想测试平台，实验视频也证明其操作极具趣味性。
原文摘要 · Abstract (English)
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.
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