Quadruped robots face limitations in long-range navigation efficiency due to their reliance on legs. To ameliorate the limitations, we introduce a Reinforcement Learning-based Active Transporter Riding method (\textit{RL-ATR}), inspired by humans' utilization of personal transporters, including Segways. The \textit{RL-ATR} features a transporter riding policy and two state estimators. The policy devises adequate maneuvering strategies according to transporter-specific control dynamics, while the estimators resolve sensor ambiguities in non-inertial frames by inferring unobservable robot and transporter states. Comprehensive evaluations in simulation validate proficient command tracking abilities across various transporter-robot models and reduced energy consumption compared to legged locomotion. Moreover, we conduct ablation studies to quantify individual component contributions within the \textit{RL-ATR}. This riding ability could broaden the locomotion modalities of quadruped robots, potentially expanding the operational range and efficiency.

翻译：四足机器人因其对腿部运动的依赖，在长距离导航效率方面存在局限。为改善此限制，受人类使用包括赛格威在内的个人交通工具的启发，我们提出了一种基于强化学习的主动载具骑行方法（\textit{RL-ATR}）。该方法包含一个载具骑行策略和两个状态估计器。该策略根据载具特定的控制动力学设计适当的操控策略，而两个估计器则通过推断不可观测的机器人与载具状态，解决非惯性参考系中的传感器模糊性问题。仿真中的综合评估验证了该方法在不同载具-机器人模型上均具备熟练的指令跟踪能力，并且与纯腿部运动相比降低了能耗。此外，我们进行了消融研究以量化\textit{RL-ATR}内部各独立组件的贡献。这种骑行能力有望拓宽四足机器人的运动模态，从而可能扩展其操作范围并提升效率。