Learning-Based Modeling of Soft Robots via Cosserat Rod Theory

• URL: http://arxiv.org/abs/2606.20958v1
• Date: Thu, 18 Jun 2026 21:53:53 GMT
• Title: Learning-Based Modeling of Soft Robots via Cosserat Rod Theory
• Abstract summary: This work introduces a port-Hamiltonian Gaussian Process Regression framework for learning and simulating the dynamics of planar, rod-like soft robots.<n>In detail, the proposed model integrates Cosserat rod theory and Hamiltonian physics with data-driven inference to preserve the system's energy structure.<n> Numerical simulations show that we can achieve accurate and energy-consistent representations of a rod-like soft robot.
• Score: 0.05249805590164902
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Modeling soft robot dynamics is challenging due to their continuum structure and typically nonlinear dynamics. Creating models based on first-order principles is typically time-demanding, and their expressiveness is limited, whereas data-driven models lack interpretability and physical consistency. This work aims to overcome these challenges by introducing a port-Hamiltonian Gaussian Process Regression framework for learning and simulating the dynamics of planar, rod-like soft robots. In detail, the proposed model integrates Cosserat rod theory and Hamiltonian physics with data-driven inference to preserve the system's energy structure while accurately learning the rod dynamics. Numerical simulations show that we can achieve accurate and energy-consistent representations of a rod-like soft robot, showing the potential for a robust and interpretable pathway for modeling complex continuum mechanics.

Related papers

• Trajectory Generation for Underactuated Soft Robot Manipulators using Discrete Elastic Rod Dynamics [2.739506394240299]
  Planning motions with dynamically-feasible trajectories requires models that capture arbitrary deformations.<n>We present our trajectory generation framework and validate it experimentally on a pneumatic soft robotic limb.
  arXiv  Detail & Related papers  (2026-03-23T22:01:14Z)
• Diffusion Dynamics Models with Generative State Estimation for Cloth Manipulation [31.868248649812088]
  Cloth manipulation is challenging due to its highly complex dynamics, near-infinite degrees of freedom, and frequent self-occlusions.<n>We propose a diffusion-based generative approach for both perception and dynamics modeling.<n>We show that our framework enables effective cloth folding on real robotic systems.
  arXiv  Detail & Related papers  (2025-03-15T05:34:26Z)
• Stable Port-Hamiltonian Neural Networks [12.84178151785869]
  This article introduces stable port-Hamiltonian neural networks, a machine learning architecture that incorporates physical biases of energy conservation and dissipation.<n>We demonstrate that these strong inductive biases facilitate robust learning of stable dynamics from sparse data, while avoiding instability and surpassing purely data-driven approaches in accuracy and physically meaningful generalization.
  arXiv  Detail & Related papers  (2025-02-04T16:57:02Z)
• GausSim: Foreseeing Reality by Gaussian Simulator for Elastic Objects [55.02281855589641]
  GausSim is a novel neural network-based simulator designed to capture the dynamic behaviors of real-world elastic objects represented through Gaussian kernels.<n>We leverage continuum mechanics and treat each kernel as a Center of Mass System (CMS) that represents continuous piece of matter.<n>In addition, GausSim incorporates explicit physics constraints, such as mass and momentum conservation, ensuring interpretable results and robust, physically plausible simulations.
  arXiv  Detail & Related papers  (2024-12-23T18:58:17Z)
• Probabilistic Latent Variable Modeling for Dynamic Friction Identification and Estimation [2.638878351659023]
  Identification of dynamic models in robotics is essential to support control design, friction compensation, output torque estimation.<n>We propose to account for unidentified dynamics in the robot joints using latent dynamic states.<n>We use the Expectation-Maximisation (EM) algorithm to find a Likelihood Maximum Estimate (MLE) of the model parameters.
  arXiv  Detail & Related papers  (2024-12-20T10:16:18Z)
• Physics-Constrained Learning for PDE Systems with Uncertainty Quantified Port-Hamiltonian Models [0.7350858947639451]
  We propose a physics-constrained learning method that combines powerful learning tools and reliable physical models. Based on the Bayesian nature of the Gaussian process, we not only learn the dynamics of the system, but also enable uncertainty quantification.
  arXiv  Detail & Related papers  (2024-06-17T17:52:01Z)
• Discovering Interpretable Physical Models using Symbolic Regression and Discrete Exterior Calculus [55.2480439325792]
  We propose a framework that combines Symbolic Regression (SR) and Discrete Exterior Calculus (DEC) for the automated discovery of physical models. DEC provides building blocks for the discrete analogue of field theories, which are beyond the state-of-the-art applications of SR to physical problems. We prove the effectiveness of our methodology by re-discovering three models of Continuum Physics from synthetic experimental data.
  arXiv  Detail & Related papers  (2023-10-10T13:23:05Z)
• Exploring Model Transferability through the Lens of Potential Energy [78.60851825944212]
  Transfer learning has become crucial in computer vision tasks due to the vast availability of pre-trained deep learning models. Existing methods for measuring the transferability of pre-trained models rely on statistical correlations between encoded static features and task labels. We present an insightful physics-inspired approach named PED to address these challenges.
  arXiv  Detail & Related papers  (2023-08-29T07:15:57Z)
• Physics-Inspired Temporal Learning of Quadrotor Dynamics for Accurate Model Predictive Trajectory Tracking [76.27433308688592]
  Accurately modeling quadrotor's system dynamics is critical for guaranteeing agile, safe, and stable navigation. We present a novel Physics-Inspired Temporal Convolutional Network (PI-TCN) approach to learning quadrotor's system dynamics purely from robot experience. Our approach combines the expressive power of sparse temporal convolutions and dense feed-forward connections to make accurate system predictions.
  arXiv  Detail & Related papers  (2022-06-07T13:51:35Z)
• Constructing Neural Network-Based Models for Simulating Dynamical Systems [59.0861954179401]
  Data-driven modeling is an alternative paradigm that seeks to learn an approximation of the dynamics of a system using observations of the true system. This paper provides a survey of the different ways to construct models of dynamical systems using neural networks. In addition to the basic overview, we review the related literature and outline the most significant challenges from numerical simulations that this modeling paradigm must overcome.
  arXiv  Detail & Related papers  (2021-11-02T10:51:42Z)
• Physics-Integrated Variational Autoencoders for Robust and Interpretable Generative Modeling [86.9726984929758]
  We focus on the integration of incomplete physics models into deep generative models. We propose a VAE architecture in which a part of the latent space is grounded by physics. We demonstrate generative performance improvements over a set of synthetic and real-world datasets.
  arXiv  Detail & Related papers  (2021-02-25T20:28:52Z)

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.