用神经微分方程建模水下机器人，实现自适应运动决策
Learning Adaptive Hydrodynamic Models Using Neural ODEs in Complex Conditions
- 基于神经微分方程与注意力机制，实时处理传感器数据
- 在不同水流速度下准确预测受力状态，提升水下运动能力
- 适合研究水陆两栖机器人自主控制的学者与工程师
基于强化学习的四足机器人在多种地形表现优异，但在复杂水下环境中仍缺乏游泳能力。本文提出一种数据驱动的水动力学模型，用于增强两栖四足机器人的自适应能力。该模型结合神经常微分方程（Neural ODEs）与注意力机制，可准确处理实时传感器数据，理解并预测复杂环境变化，支持鲁棒的决策策略。我们利用真实环境下的传感器数据，涵盖多种环境参数和内部状态变量，训练并评估该模型。重点测试了机器人在不同水动力条件下的表现，评估其在多种速度与流体动态条件下的适应能力。结果表明，模型能有效学习并适应变化条件，精准预测力状态，显著提升机器人在实际场景中的自主行为能力。
原文摘要 · Abstract (English)
Reinforcement learning-based quadruped robots excel across various terrains but still lack the ability to swim in water due to the complex underwater environment. This paper presents the development and evaluation of a data-driven hydrodynamic model for amphibious quadruped robots, aiming to enhance their adaptive capabilities in complex and dynamic underwater environments. The proposed model leverages Neural Ordinary Differential Equations (ODEs) combined with attention mechanisms to accurately process and interpret real-time sensor data. The model enables the quadruped robots to understand and predict complex environmental patterns, facilitating robust decision-making strategies. We harness real-time sensor data, capturing various environmental and internal state parameters to train and evaluate our model. A significant focus of our evaluation involves testing the quadruped robot's performance across different hydrodynamic conditions and assessing its capabilities at varying speeds and fluid dynamic conditions. The outcomes suggest that the model can effectively learn and adapt to varying conditions, enabling the prediction of force states and enhancing autonomous robotic behaviors in various practical scenarios.
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