用形态相似性实现软体机器鱼动力学的高效迁移学习
Efficient Transfer Learning of Robot Dynamic Models Using Morphological Similarity
- 基于自编码器构建共享潜在表征，对齐大小不同机器鱼的动力学
- 仅需少量标签数据即可在小型机器人上实现精确状态估计
- 适合跨平台软体水下机器人快速建模，尤其适用于数据稀缺场景
本研究提出一种基于神经网络的迁移学习框架，用于建模软体鳍状推进水下机器人的动力学特性。针对形态相似但尺度与流体特性不同的机器人，将大型机器人（源域）训练所得模型，适配至小型机器人（目标域），仅需少量标注数据。为实现标签高效的迁移，设计了一种基于自编码器的域适应方法，学习两者动力学的共享潜在表示。在两台真实水下机器人上的实验表明，该方法可在无标签数据情况下，准确估计目标平台的机体坐标系速度，展现出在形态相似平台间高效跨机器人动力学迁移的巨大潜力。
原文摘要 · Abstract (English)
This study proposes a neural network-based transfer learning framework for modeling the dynamics of soft, fin-actuated underwater robots. We focus on morphologically similar robots that differ in scale and hydrodynamic properties. A model trained on data from a larger robot (source domain) is adapted to a smaller one (target domain) with limited labeled data. To enable label-efficient transfer, we develop an autoencoder-based domain adaptation approach that learns a shared latent representation aligning the dynamics of both robots. Experiments on two real underwater robots show that the proposed method enables accurate state estimation of the body-frame velocities on a target platform without labeled data, highlighting its potential for efficient cross-robot dynamics transfer among morphologically similar platforms.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。