複雑環境下での適応型流体力学モデルをNeural ODEで学習
Learning Adaptive Hydrodynamic Models Using Neural ODEs in Complex Conditions
水陸両用四足ロボットの水中適応能力を高めるため、Neural ODEとアテンション機構を組み合わせたデータ駆動型流体力学モデルを開発し、リアルタイムセンサデータから力状態を予測できることを示した。
著者: Cong Wang, Aoming Liang, Fei Han, Xinyu Zeng, Zhibin Li, Dixia Fan, Jens Kober
分類: cs.RO, cs.AI
原文アブストラクト
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.
