A quadruped robot faces balancing challenges on a six-degrees-of-freedom moving platform, like subways, buses, airplanes, and yachts, due to independent platform motions and resultant diverse inertia forces on the robot. To alleviate these challenges, we present the Learning-based Active Stabilization on Moving Platforms (\textit{LAS-MP}), featuring a self-balancing policy and system state estimators. The policy adaptively adjusts the robot's posture in response to the platform's motion. The estimators infer robot and platform states based on proprioceptive sensor data. For a systematic training scheme across various platform motions, we introduce platform trajectory generation and scheduling methods. Our evaluation demonstrates superior balancing performance across multiple metrics compared to three baselines. Furthermore, we conduct a detailed analysis of the \textit{LAS-MP}, including ablation studies and evaluation of the estimators, to validate the effectiveness of each component.

翻译：四足机器人在六自由度移动平台（如地铁、公交车、飞机和游艇）上因平台独立运动及由此产生的多样化惯性力而面临平衡挑战。为缓解这些挑战，我们提出了基于学习的移动平台主动稳定方法（\textit{LAS-MP}），该方法包含自平衡策略与系统状态估计器。该策略能根据平台运动自适应调整机器人姿态。估计器基于本体感知传感器数据推断机器人与平台状态。为构建适用于多种平台运动的系统化训练方案，我们提出了平台轨迹生成与调度方法。评估结果表明，与三种基线方法相比，本方法在多项指标上均展现出更优的平衡性能。此外，我们对\textit{LAS-MP}进行了详细分析，包括消融实验和估计器评估，以验证各组成部分的有效性。