让四足机器人骑上个人交通工具，提升长距离导航效率。
Enhancing Navigation Efficiency of Quadruped Robots via Leveraging Personal Transportation Platforms
- 用强化学习设计骑乘策略，适配不同运输工具的控制特性。
- 仿真测试显示能耗比纯步行降低，且能精准跟踪指令。
- 适合需要远距离移动的四足机器人应用场景。
四足机器人因依赖腿部运动，在长距离导航中存在效率瓶颈。为此，我们提出一种基于强化学习的主动运输平台骑乘方法（RL-ATR），灵感来自人类使用平衡车等个人交通工具。RL-ATR 包含骑乘策略与两个状态估计器：策略根据运输工具的控制动态制定合理操控方案；估计器通过推断不可观测状态，解决非惯性参考系下的传感器模糊问题。仿真评估表明，该方法在多种运输工具-机器人组合下均具备优异的指令跟踪能力，并显著降低能耗。消融实验量化了各组件贡献。此项能力可拓展四足机器人的运动模式，有望大幅提高其作业范围与效率。
原文摘要 · Abstract (English)
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.
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