Efficient Transfer Learning of Robot Dynamic Models Using Morphological Similarity
This study proposes a neural network-based transfer learning framework for modeling the dynamics of soft, fin-actuated underwater robots. Using an autoencoder-based domain adaptation approach, a model trained on a larger robot is adapted to a smaller one without labeled data, achieving accurate body-frame velocity estimation. The work demonstrates efficient cross-robot dynamics transfer among morphologically similar platforms.
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[Submitted on 6 Jul 2026]
Title:Efficient Transfer Learning of Robot Dynamic Models Using Morphological Similarity
View a PDF of the paper titled Efficient Transfer Learning of Robot Dynamic Models Using Morphological Similarity, by Pavlo Kupyn and 4 other authors
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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.
Comments: Accepted for publication in the 2026 12th International Conference on Control, Decision and Information Technologies (CoDIT)
Subjects:
Robotics (cs.RO)
Cite as: arXiv:2607.05665 [cs.RO]
(or arXiv:2607.05665v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2607.05665
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Pavlo Kupyn [view email] [v1] Mon, 6 Jul 2026 22:14:49 UTC (7,555 KB)
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