Py-DiSMech: A Scalable and Efficient Framework for Discrete Differential Geometry-Based Modeling and Control of Soft Robots

• URL: http://arxiv.org/abs/2512.09911v2
• Date: Sat, 20 Dec 2025 18:42:01 GMT
• Title: Py-DiSMech: A Scalable and Efficient Framework for Discrete Differential Geometry-Based Modeling and Control of Soft Robots
• Abstract summary: Py-DiSMech is a Python-based, open-source simulation framework for modeling and control of soft robotic structures.<n>By discretizing geometric quantities such as curvature and strain directly on meshes, Py-DiSMech captures the nonlinear deformation of rods, shells, and hybrid structures with high fidelity and reduced computational cost.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: High-fidelity simulation has become essential to the design and control of soft robots, where large geometric deformations and complex contact interactions challenge conventional modeling tools. Recent advances in the field demand simulation frameworks that combine physical accuracy, computational scalability, and seamless integration with modern control and optimization pipelines. In this work, we present Py-DiSMech, a Python-based, open-source simulation framework for modeling and control of soft robotic structures grounded in the principles of Discrete Differential Geometry (DDG). By discretizing geometric quantities such as curvature and strain directly on meshes, Py-DiSMech captures the nonlinear deformation of rods, shells, and hybrid structures with high fidelity and reduced computational cost. The framework introduces (i) a fully vectorized NumPy implementation achieving order-of-magnitude speed-ups over existing geometry-based simulators; (ii) a penalty-energy-based fully implicit contact model that supports rod-rod, rod-shell, and shell-shell interactions; (iii) a natural-strain-based feedback-control module featuring a proportional-integral (PI) controller for shape regulation and trajectory tracking; and (iv) a modular, object-oriented software design enabling user-defined elastic energies, actuation schemes, and integration with machine-learning libraries. Benchmark comparisons demonstrate that Py-DiSMech substantially outperforms the state-of-the-art simulator Elastica in computational efficiency while maintaining physical accuracy. Together, these features establish Py-DiSMech as a scalable, extensible platform for simulation-driven design, control validation, and sim-to-real research in soft robotics.

Related papers

• In-Context Model Predictive Generation: Open-Vocabulary Motion Synthesis from Language Models to Physics [109.99871326497333]
  In-Context Model Predictive Generation (ICMPG) is a framework that integrates language-model planning with inference-time physical feedback.<n>ICMPG reformulates motion synthesis as a Model Predictive Control (MPC)-like process with two modules.
  arXiv  Detail & Related papers  (2026-06-25T12:50:33Z)
• Simulation-Driven Evolutionary Motion Parameterization for Contact-Rich Granular Scooping with a Soft Conical Robotic Hand [14.911239660367926]
  Tool-based scooping is vital in robot-assisted tasks, enabling interaction with objects of varying sizes, shapes, and material states.<n>Recent studies have shown that flexible, reconfigurable soft robotic end-effectors can adapt their shape to maintain consistent contact during scooping.<n>These soft tools can adjust to varying container sizes and materials without requiring complex sensing or control.<n>This paper presents the development of a physics-based simulation model of a deformable soft conical robotic hand.
  arXiv  Detail & Related papers  (2026-04-07T07:30:21Z)
• A General Lie-Group Framework for Continuum Soft Robot Modeling [3.0588867238694157]
  This paper introduces a general Lie group framework for modeling continuum soft robots.<n>The framework is extended to handle complex robotic structures, including segmented, branched, nested, and rigid-soft composite configurations.
  arXiv  Detail & Related papers  (2026-03-09T11:03:21Z)
• A Unified Low-Dimensional Design Embedding for Joint Optimization of Shape, Material, and Actuation in Soft Robots [4.829343342713712]
  We introduce a smooth, low-dimensional design embedding for soft robots that unifies shape morphing, multi-material distribution, and actuation.<n>In experiments, we show that design increases with the number of basis functions, unlike comparable neural network encodings.<n>We further show that joint co-optimization of shape, material, and actuation using our unified embedding consistently outperforms sequential strategies.
  arXiv  Detail & Related papers  (2026-03-06T17:33:32Z)
• Simulation-based Learning of Electrical Cabinet Assembly Using Robot Skills [2.900269335003268]
  This paper presents a simulation-driven approach for automating the force-controlled assembly of electrical terminals on DIN-rails.<n>The proposed method integrates deep reinforcement learning (DRL) with parameterizable robot skills in a physics-based simulation environment.<n> Experimental results demonstrate high success rates (up to 100%) in both simulation and real-world settings, even under significant positional and rotational deviations.
  arXiv  Detail & Related papers  (2026-02-16T08:45:54Z)
• SORS: A Modular, High-Fidelity Simulator for Soft Robots [5.660024841218955]
  Soft robots pose unique modeling challenges due to their large nonlinear deformations, material incompressibility, and contact interactions.<n>We present SORS, a versatile, high-fidelity simulator designed to handle these complexities for soft robot applications.
  arXiv  Detail & Related papers  (2025-12-17T21:58:46Z)
• TIGeR: Tool-Integrated Geometric Reasoning in Vision-Language Models for Robotics [53.442362491589726]
  We present TIGeR (Tool-Integrated Geometric Reasoning), a novel framework that transforms Vision-Language Models (VLMs) into geometric computers.<n>Rather than attempting to internalize complex geometric operations within neural networks, TIGeR empowers models to recognize geometric reasoning requirements.<n>We show that TIGeR achieves SOTA performance on geometric reasoning benchmarks while demonstrating centimeter-level precision in real-world robotic manipulation tasks.
  arXiv  Detail & Related papers  (2025-10-08T16:20:23Z)
• G-Sim: Generative Simulations with Large Language Models and Gradient-Free Calibration [48.948187359727996]
  G-Sim is a hybrid framework that automates simulator construction with rigorous empirical calibration.<n>It produces reliable, causally-informed simulators, mitigating data-inefficiency and enabling robust system-level interventions.
  arXiv  Detail & Related papers  (2025-06-10T22:14:34Z)
• 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)
• A Realistic Simulation Framework for Analog/Digital Neuromorphic Architectures [73.65190161312555]
  ARCANA is a software spiking neural network simulator designed to account for the properties of mixed-signal neuromorphic circuits.<n>We show how the results obtained provide a reliable estimate of the behavior of the spiking neural network trained in software, once deployed in hardware.
  arXiv  Detail & Related papers  (2024-09-23T11:16:46Z)
• Software tool-set for automated quantum system identification and device bring up [0.0]
  We present a software tool-set which combines the theoretical, optimal control view of quantum devices with the practical operation and characterization tasks.<n>We perform model-based simulations to create control schemes, calibrate these controls in a closed-loop with the device.<n>Finally, we improve the system model through minimization of the mismatch between simulation and experiment, resulting in a digital twin of the device.
  arXiv  Detail & Related papers  (2022-05-10T12:06:53Z)
• Real-time Neural-MPC: Deep Learning Model Predictive Control for Quadrotors and Agile Robotic Platforms [59.03426963238452]
  We present Real-time Neural MPC, a framework to efficiently integrate large, complex neural network architectures as dynamics models within a model-predictive control pipeline. We show the feasibility of our framework on real-world problems by reducing the positional tracking error by up to 82% when compared to state-of-the-art MPC approaches without neural network dynamics.
  arXiv  Detail & Related papers  (2022-03-15T09:38:15Z)

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.