{"ID":6024156,"CreatedAt":"2026-07-08T01:00:23.257252134Z","UpdatedAt":"2026-07-09T22:11:23.825470046Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.05665","arxiv_id":"2607.05665","title":"Efficient Transfer Learning of Robot Dynamic Models Using Morphological Similarity","abstract":"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.","short_abstract":"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 (...","url_abs":"https://arxiv.org/abs/2607.05665","url_pdf":"https://arxiv.org/pdf/2607.05665v1","authors":"[\"Pavlo Kupyn\",\"Yuya Hamamatsu\",\"Roza Gkliva\",\"Asko Ristolainen\",\"Maarja Kruusmaa\"]","published":"2026-07-06T22:14:49Z","proceeding":"cs.RO","tasks":"[\"cs.RO\"]","methods":"[]","has_code":false}