Dataset of simulated deformations of a three-fiber soft robotic arm modeled based on morphoelasticity and active filament theory

2025/10/03 by Carina Veil, Moritz Flaschel, Veil, Carina +3 · 1 citation
Computer Science · Engineering · #Computational Geometry and Mesh Generation #FOS: Computer and information sciences #Modular Robots and Swarm Intelligence #Robotic Path Planning Algorithms #Robotics (cs.RO)

paper · pdf · doi:10.48550/arxiv.2510.03547

openalex publication_date 2025/10/03 · openalex created_date 2025/10/09 · openalex updated_date 2026/07/28

Abstract

Dataset Description This dataset contains simulation data of a three-fiber soft robotic arm generated using a custom Julia package (Paper: https://www.sciencedirect.com/science/article/pii/S0022509622001181 , GitHub: https://github.com/LivingMatterLab/ActiveFilaments.jl ). Each simulation defines a set of fiber activation inputs γi and solves a boundary value problem to obtain the robot’s centerline configuration (x, y, z) along the spatial coordinate z ∈ [0, L]. A total of N = 1,000,000 simulations were performed using random activation triplets within the physical range γi ∈ [-1.67, 0]. The resulting dataset provides a large-scale mapping from actuation inputs to corresponding robot shapes. Contents The file load_data.py loads the following data: activations_gamma.npz: Array of shape N × 3, containing the activation inputs γ1, γ2, γ3. centerline_r.npz: Array of shape N × 100 × 3, giving the computed centerline positions in Cartesian coordinates (x, y, z) at 100 discretized spatial points. coordinate_z.npz: Array of shape N × 100, specifying the discretized spatial coordinate z for each simulation. Usage The data has been employed in the following publications: Shape-Space Graphs: Fast and Collision-Free Path Planning for Soft RobotsThe dataset serves as the shape-library for constructing shape-space graphs.Paper: https://arxiv.org/abs/2510.03547 GitHub: https://github.com/cayobro/shape-space-graphs.git Infinite-Dimensional Closed-Loop Inverse Kinematics for Soft Robots via Neural OperatorsThe dataset is used for training and validation of the neural operator model.Paper: https://arxiv.org/abs/2602.18655 GitHub: https://github.com/cayobro/soft-robot-CLIK-via-neural-operators.git Citation If you use this dataset, please consider citing the above works.

Citations

• Material Fingerprinting: A shortcut to material model discovery without solving optimization problems
• 3D Steering and Localization in Pipes and Burrows using an Externally Steered Soft Growing Robot
• Neural Configuration-Space Barriers for Manipulation Planning and Control
• Neural Configuration Distance Function for Continuum Robot Control
• Configuration Space Distance Fields for Manipulation Planning
• Resilient Movement Planning for Continuum Robots
• Minimal design of the elephant trunk as an active filament
• Efficient RRT*-based Safety-Constrained Motion Planning for Continuum Robots in Dynamic Environments
• Representing Robot Geometry as Distance Fields: Applications to Whole-body Manipulation
• Learning Models as Functionals of Signed-Distance Fields for Manipulation Planning
• Near-optimal Smooth Path Planning for Multisection Continuum Arms
• Sampling-based Algorithms for Optimal Motion Planning
• Sampling-based algorithms for optimal motion planning
• Real-Time Obstacle Avoidance for Manipulators and Mobile Robots
• A Note on Two Problems in Connexion with Graphs

Cited by

• Force-Safe Environment Maps and Real-Time Detection for Soft Robot Manipulators

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