Efficient Transfer Learning of Robot Dynamic Models Using Morphological Similarity

• URL: http://arxiv.org/abs/2607.05665v1
• Date: Mon, 06 Jul 2026 22:14:49 GMT
• Title: Efficient Transfer Learning of Robot Dynamic Models Using Morphological Similarity
• Abstract summary: We focus on morphologically similar robots that differ in scale and hydrodynamic properties.<n>A model trained on data from a larger robot is adapted to a smaller one with limited labeled data.<n> Experiments on two real underwater robots show that the proposed method enables accurate state estimation of the body-frame velocities.
• Score: 1.7616042687330642
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Data-Driven Dynamic Parameter Learning of manipulator robots [0.8679862302950613]
  We propose a Transformer-based approach for dynamic parameter estimation.<n>The dataset consists of 8,192 robots with varied inertial and frictional properties.<n>Our model effectively captures both temporal and spatial dependencies.
  arXiv  Detail & Related papers  (2025-12-09T16:15:58Z)
• Action Flow Matching for Continual Robot Learning [54.10050120844738]
  Continual learning in robotics seeks systems that can constantly adapt to changing environments and tasks.<n>We introduce a generative framework leveraging flow matching for online robot dynamics model alignment.<n>We find that by transforming the actions themselves rather than exploring with a misaligned model, the robot collects informative data more efficiently.
  arXiv  Detail & Related papers  (2025-04-25T16:26:15Z)
• Learning Adaptive Hydrodynamic Models Using Neural ODEs in Complex Conditions [9.392180262607921]
  Reinforcement learning-based quadruped robots excel across various terrains but still lack the ability to swim in water. This paper presents the development and evaluation of a data-driven hydrodynamic model for amphibious quadruped robots.
  arXiv  Detail & Related papers  (2024-10-01T08:18:36Z)
• Learning a Terrain- and Robot-Aware Dynamics Model for Autonomous Mobile Robot Navigation [8.261491880782769]
  We propose a novel approach for learning a probabilistic, terrain- and robot-aware forward dynamics model (TRADYN) We evaluate our method in simulation for 2D navigation of a robot with uni-cycle dynamics with varying properties on terrain with spatially varying friction coefficients.
  arXiv  Detail & Related papers  (2024-09-17T16:46:39Z)
• Navigating the Human Maze: Real-Time Robot Pathfinding with Generative Imitation Learning [0.0]
  We introduce goal-conditioned autoregressive models to generate crowd behaviors, capturing intricate interactions among individuals. The model processes potential robot trajectory samples and predicts the reactions of surrounding individuals, enabling proactive robotic navigation in complex scenarios.
  arXiv  Detail & Related papers  (2024-08-07T14:32:41Z)
• Comparative Evaluation of Learning Models for Bionic Robots: Non-Linear Transfer Function Identifications [0.0]
  The control and modeling of robot dynamics have increasingly adopted model-free control strategies using machine learning. This research introduces a comprehensive evaluation strategy and framework for the application of model-free control.
  arXiv  Detail & Related papers  (2024-07-02T17:00:23Z)
• DiffuseBot: Breeding Soft Robots With Physics-Augmented Generative Diffusion Models [102.13968267347553]
  We present DiffuseBot, a physics-augmented diffusion model that generates soft robot morphologies capable of excelling in a wide spectrum of tasks. We showcase a range of simulated and fabricated robots along with their capabilities.
  arXiv  Detail & Related papers  (2023-11-28T18:58:48Z)
• Cross-domain Transfer Learning and State Inference for Soft Robots via a Semi-supervised Sequential Variational Bayes Framework [7.608532769380028]
  We propose a semi-supervised sequential variational Bayes (DSVB) framework for transfer learning and state inference in soft robots with missing state labels. Unlike existing transfer learning approaches, our proposed DSVB employs a recurrent neural network to model the nonlinear dynamics and temporal coherence in soft robot data.
  arXiv  Detail & Related papers  (2023-03-03T03:17:53Z)
• Universal Morphology Control via Contextual Modulation [52.742056836818136]
  Learning a universal policy across different robot morphologies can significantly improve learning efficiency and generalization in continuous control. Existing methods utilize graph neural networks or transformers to handle heterogeneous state and action spaces across different morphologies. We propose a hierarchical architecture to better model this dependency via contextual modulation.
  arXiv  Detail & Related papers  (2023-02-22T00:04:12Z)
• Real-to-Sim: Predicting Residual Errors of Robotic Systems with Sparse Data using a Learning-based Unscented Kalman Filter [65.93205328894608]
  We learn the residual errors between a dynamic and/or simulator model and the real robot. We show that with the learned residual errors, we can further close the reality gap between dynamic models, simulations, and actual hardware.
  arXiv  Detail & Related papers  (2022-09-07T15:15:12Z)
• Gradient-Based Trajectory Optimization With Learned Dynamics [80.41791191022139]
  We use machine learning techniques to learn a differentiable dynamics model of the system from data. We show that a neural network can model highly nonlinear behaviors accurately for large time horizons. In our hardware experiments, we demonstrate that our learned model can represent complex dynamics for both the Spot and Radio-controlled (RC) car.
  arXiv  Detail & Related papers  (2022-04-09T22:07:34Z)
• Bayesian Meta-Learning for Few-Shot Policy Adaptation Across Robotic Platforms [60.59764170868101]
  Reinforcement learning methods can achieve significant performance but require a large amount of training data collected on the same robotic platform. We formulate it as a few-shot meta-learning problem where the goal is to find a model that captures the common structure shared across different robotic platforms. We experimentally evaluate our framework on a simulated reaching and a real-robot picking task using 400 simulated robots.
  arXiv  Detail & Related papers  (2021-03-05T14:16:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.