让四足机器人在晃动平台上自动平衡，靠学习自适应控制。
Learning-based Adaptive Control of Quadruped Robots for Active Stabilization on Moving Platforms
- 用学习方法自适应调整姿态应对平台运动
- 在多种移动平台上平衡表现优于三个基线
- 适合需要在颠簸环境中稳定行走的机器人应用
四足机器人在六自由度移动平台（如地铁、巴士、飞机和游艇）上面临平衡挑战，因平台独立运动导致机器人受力复杂多变。为缓解此问题，本文提出基于学习的移动平台主动稳定系统（LAS-MP），包含自平衡策略与状态估计算法。策略根据平台运动动态调整机器人姿态，估计算法通过本体感知数据推断机器人与平台状态。为实现跨多种平台运动的系统化训练，引入平台轨迹生成与调度方法。实验表明，该系统在多项指标上显著优于三个基线模型。此外，通过消融实验与估计算法评估，验证了各模块的有效性。
原文摘要 · Abstract (English)
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.
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