SOFTMAP: Sim2Real Soft Robot Forward Modeling via Topological Mesh Alignment and Physics Prior

• URL: http://arxiv.org/abs/2603.19384v1
• Date: Thu, 19 Mar 2026 18:21:44 GMT
• Title: SOFTMAP: Sim2Real Soft Robot Forward Modeling via Topological Mesh Alignment and Physics Prior
• Abstract summary: SOFTMAP is a sim-real learning framework for real-time 3D forward modeling of tendon-actuated soft finger manipulators.<n>We evaluate SOFTMAP on both simulated and physical hardware, achieving shape prediction accuracy with a Chamfer distance of 0.389 mm in simulation.<n>Our results show that SOFTMAP provides a data-efficient approach for 3D forward modeling and control of soft manipulators.
• Score: 21.30232105970865
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: While soft robot manipulators offer compelling advantages over rigid counterparts, including inherent compliance, safe human-robot interaction, and the ability to conform to complex geometries, accurate forward modeling from low-dimensional actuation commands remains an open challenge due to nonlinear material phenomena such as hysteresis and manufacturing variability. We present SOFTMAP, a sim-to-real learning framework for real-time 3D forward modeling of tendon-actuated soft finger manipulators. SOFTMAP combines four components: (1) As-Rigid-As-Possible (ARAP)-based topological alignment that projects simulated and real point clouds into a shared, topologically consistent vertex space; (2) a lightweight MLP forward model pretrained on simulation data to map servo commands to full 3D finger geometry; (3) a residual correction network trained on a small set of real observations to predict per-vertex displacement fields that compensate for sim-to-real discrepancies; and (4) a closed-form linear actuation calibration layer enabling real-time inference at 30 FPS. We evaluate SOFTMAP on both simulated and physical hardware, achieving state-of-the-art shape prediction accuracy with a Chamfer distance of 0.389 mm in simulation and 3.786 mm on hardware, millimeter-level fingertip trajectory tracking across multiple target paths, and a 36.5% improvement in teleoperation task success over the baseline. Our results show that SOFTMAP provides a data-efficient approach for 3D forward modeling and control of soft manipulators.

Related papers

• Task-space model-based control of pneumatic soft actuators [1.3707825707652799]
  Soft actuators enable dexterous and compliant interaction, but closed-loop task-space control remains challenging.<n>This paper presents a real-time dynamic-model-based task-space feedback and estimation framework based on a non-minimal coordinate discrete elastic rod model.<n>Results demonstrate that structured, non-minimal dynamic models can enable real-time, high-precision, moderate-bandwidth task-space control of planar soft pneumatic actuators in free space.
  arXiv  Detail & Related papers  (2026-08-27T14:32:07Z)
• MANGO-Grasp: Mahalanobis Fields over Geometry-Oriented 3D Gaussians for Cross-Embodiment Dexterous Grasping [82.63833405368159]
  Cross-embodiment dexterous grasping aims to synthesize stable grasps across heterogeneous multi-fingered hands with little or no embodiment-specific tuning.<n>We propose MANGO-Grasp, an anisotropic interaction framework that represents objects as geometry-oriented 3D Gaussian primitives and robot hands as surface keypoints encoded into morpho-kinematic descriptors.
  arXiv  Detail & Related papers  (2026-08-03T10:12:15Z)
• Enabling Robust Cloth Manipulation via Inference-Time Simulator-in-the-Loop Refinement [16.29450701834732]
  We use a physical simulator as a backend rollout engine to evaluate candidate trajectories in parallel and refine nominal actions online.<n>We develop a real-to-sim module, trained purely on synthetic data, that maps a single RGB observation to simulation-compatible cloth state.<n>Real-robot experiments show higher success rates and stronger than baseline methods.
  arXiv  Detail & Related papers  (2026-06-23T13:20:12Z)
• TaCauchy: An Extensible FEM Framework for Vision-Based Tactile Simulation [18.855496219309]
  Vision-based tactile sensors require high-fidelity simulation for reinforcement learning.<n>We present TaCauchy, a framework that integrates rigorous physics-based force computation into Isaac Sim.
  arXiv  Detail & Related papers  (2026-06-18T16:08:45Z)
• URDF-Anything+: Autoregressive Articulated 3D Models Generation for Physical Simulation [45.4820195450296]
  We present an end-to-end autoregressive framework that directly generates executable articulated object models from visual observations.<n>Building on this capability, we enable a new Real-Follow-Sim paradigm, where high-fidelity digital twins constructed from visual observations allow policies trained in simulation to transfer to real robots without online adaptation.
  arXiv  Detail & Related papers  (2026-03-14T16:23:44Z)
• FastPhysGS: Accelerating Physics-based Dynamic 3DGS Simulation via Interior Completion and Adaptive Optimization [56.17833729527066]
  We propose FastPhysGS, a framework for physics-based dynamic 3DGS simulation.<n>FastPhysGS achieves high-fidelity physical simulation in 1 minute using only 7 GB runtime memory.
  arXiv  Detail & Related papers  (2026-02-02T07:00:42Z)
• DISF: Disentangled Iterative Surface Fitting for Contact-stable Grasp Planning with Grasp Pose Alignment to the Object Center of Mass [8.61285912675644]
  We propose a novel surface fitting algorithm that integrates contact stability while preserving geometric compatibility.<n>Inspired by human grasping behavior, our method disentangles the grasp pose optimization into three sequential steps.<n>We validate our approach in simulation and real-world execution on three robot--gripper platforms.
  arXiv  Detail & Related papers  (2025-12-31T01:15:09Z)
• Py-DiSMech: A Scalable and Efficient Framework for Discrete Differential Geometry-Based Modeling and Control of Soft Robots [0.0]
  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.
  arXiv  Detail & Related papers  (2025-12-10T18:40:27Z)
• URDF-Anything: Constructing Articulated Objects with 3D Multimodal Language Model [76.08429266631823]
  We propose an end-to-end automatic reconstruction framework based on a 3D multimodal large language model (MLLM)<n>URDF-Anything utilizes an autoregressive prediction framework based on point-cloud and text multimodal input to jointly optimize geometric segmentation and kinematic parameter prediction.<n> Experiments on both simulated and real-world datasets demonstrate that our method significantly outperforms existing approaches.
  arXiv  Detail & Related papers  (2025-11-02T13:45:51Z)
• Learning to Track Any Points from Human Motion [55.831218129679144]
  We propose an automated pipeline to generate pseudo-labeled training data for point tracking.<n>A point tracking model trained on AnthroTAP achieves annotated state-of-the-art performance on the TAP-Vid benchmark.
  arXiv  Detail & Related papers  (2025-07-08T17:59:58Z)
• JAX-MPM: A Learning-Augmented Differentiable Meshfree Framework for GPU-Accelerated Lagrangian Simulation and Geophysical Inverse Modeling [1.4287758028119788]
  We present JAX-MPM, a differentiable meshfree solver based on the material point method (MPM)<n>The solver adopts a hybrid Eulerian-Lagrangian framework to capture large deformations, frictional contact, and in material behavior.<n>JAX-MPM enables efficient gradient-based optimization directly through its time-stepping solvers.
  arXiv  Detail & Related papers  (2025-07-06T00:00:11Z)
• DIMM: Decoupled Multi-hierarchy Kalman Filter for 3D Object Tracking [50.038098341549095]
  State estimation is challenging for 3D object tracking with high maneuverability.<n>We propose a novel framework, DIMM, to effectively combine estimates from different motion models in each direction.<n>DIMM significantly improves the tracking accuracy of existing state estimation methods by 31.61%99.23%.
  arXiv  Detail & Related papers  (2025-05-18T10:12:41Z)
• Flatten Anything: Unsupervised Neural Surface Parameterization [76.4422287292541]
  We introduce the Flatten Anything Model (FAM), an unsupervised neural architecture to achieve global free-boundary surface parameterization. Compared with previous methods, our FAM directly operates on discrete surface points without utilizing connectivity information. Our FAM is fully-automated without the need for pre-cutting and can deal with highly-complex topologies.
  arXiv  Detail & Related papers  (2024-05-23T14:39:52Z)
• Geometry-Informed Neural Operator for Large-Scale 3D PDEs [76.06115572844882]
  We propose the geometry-informed neural operator (GINO) to learn the solution operator of large-scale partial differential equations. We successfully trained GINO to predict the pressure on car surfaces using only five hundred data points.
  arXiv  Detail & Related papers  (2023-09-01T16:59:21Z)

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.