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.
Robust Nonprehensile Object Transport with Quadruped Robots
Technical University of Munich (TUM)（慕尼黑工业大学）；Tampere University（坦佩雷大学）；University of Naples Federico II（那不勒斯费德里科二世大学）；Mohamed Bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
Hybrid Dynamics Modeling and Trajectory Planning for a Cable-Trailer System with a Quadruped Robot
School of Artificial Intelligence and Automation, Huazhong University of Science and Technology（人工智能与自动化学院，华中科技大学）；Department of Data Science, City University of Hong Kong（数据科学系，城市大学）；Embodied AI Center of Shanghai AI Lab（上海人工智能实验室具身智能中心）
Dual-Quadruped Collaborative Transportation in Narrow Environments via Safe Reinforcement Learning
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Advanced Remanufacturing & Technology Centre (ARTC)（先进再制造与技术中心）；Agency for Science, Technology and Research (A*STAR)（科技研究局）；Cheng Kar-Shun Robotics Institute, The Hong Kong University of Science and Technology（陈克舜机器人研究所，香港科技大学）
A control scheme for collaborative object transportation between a human and a quadruped robot using the MIGHTY suction cup
Institute of Computer Science, Foundation for Research and Technology–Hellas（计算机科学研究所，希腊基础研究与技术研究院）；Department of Electrical and Computer Engineering, Hellenic Mediterranean University（电气与计算机工程系，希腊地中海大学）；Department of Computer Science, University of Crete（计算机科学系，希腊克里特大学）
Sky-Drive: A Distributed Multi-Agent Simulation Platform for Human-AI Collaborative and Socially-Aware Future Transportation
Department of Civil and Environmental Engineering, University of Wisconsin-Madison（土木与环境工程系，威斯康星大学麦迪逊分校）；Lyles School of Civil and Construction Engineering, Purdue University（莱尔斯土木与建设工程学院，普渡大学）；Elmore Family School of Electrical and Computer Engineering, Purdue University（埃尔摩家族电气与计算机工程学院，普渡大学）；Department of Computer Science and Engineering, The University of Texas at Arlington（计算机科学与工程系，德克萨斯大学阿灵顿分校）；Google（谷歌）
