HydroShear: Hydroelastic Shear Simulation for Tactile Sim-to-Real Reinforcement Learning

• URL: http://arxiv.org/abs/2603.00446v1
• Date: Sat, 28 Feb 2026 03:59:33 GMT
• Title: HydroShear: Hydroelastic Shear Simulation for Tactile Sim-to-Real Reinforcement Learning
• Abstract summary: HydroShear is a non-holonomic hydroelastic tactile simulator that advances the state-of-the-art by modeling.<n>Our approach generates physics-based, computationally efficient force fields from arbitrary watertight geometries.<n>This fidelity enables zero-shot sim-to-real transfer of reinforcement learning policies across four tasks.
• Score: 8.60445826546191
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we address the problem of tactile sim-to-real policy transfer for contact-rich tasks. Existing methods primarily focus on vision-based sensors and emphasize image rendering quality while providing overly simplistic models of force and shear. Consequently, these models exhibit a large sim-to-real gap for many dexterous tasks. Here, we present HydroShear, a non-holonomic hydroelastic tactile simulator that advances the state-of-the-art by modeling: a) stick-slip transitions, b) path-dependent force and shear build up, and c) full SE(3) object-sensor interactions. HydroShear extends hydroelastic contact models using Signed Distance Functions (SDFs) to track the displacements of the on-surface points of an indenter during physical interaction with the sensor membrane. Our approach generates physics-based, computationally efficient force fields from arbitrary watertight geometries while remaining agnostic to the underlying physics engine. In experiments with GelSight Minis, HydroShear more faithfully reproduces real tactile shear compared to existing methods. This fidelity enables zero-shot sim-to-real transfer of reinforcement learning policies across four tasks: peg insertion, bin packing, book shelving for insertion, and drawer pulling for fine gripper control under slip. Our method achieves a 93% average success rate, outperforming policies trained on tactile images (34%) and alternative shear simulation methods (58%-61%).

Related papers

• Closing the Reality Gap: Zero-Shot Sim-to-Real Deployment for Dexterous Force-Based Grasping and Manipulation [17.63466252699545]
  Human-like dexterous hands offer human-level manipulation capabilities but remain difficult to train the control policies that can deploy on real hardware.<n>We present a sim-to-real reinforcement learning method that leverages dense tactile feedback combined with joint torque sensing.<n>This is the first demonstration of controllable grasping on a multi-finger dexterous hand trained entirely in simulation and transferred zero-shot on real hardware.
  arXiv  Detail & Related papers  (2026-07-06T11:15:36Z)
• 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)
• TactSpace: Learning a Physics-enriched Shared Latent Space for Tactile Sim-to-Real Transfer [8.357674069719524]
  We propose a multi-modal representation learning framework that aligns heterogeneous tactile modalities within a shared latent space.<n>Our approach employs modality-specific encoders to project diverse tactile observations into a common embedding space.<n>Our results demonstrate zero-shot sim-to-real transfer across physically dissimilar representations.
  arXiv  Detail & Related papers  (2026-06-17T11:41:27Z)
• Beyond Binary: Sim-to-Real Dexterous Manipulation with Physics-Grounded Contact Representation [51.463992799813816]
  A primary bottleneck in contact-rich manipulation is the difficulty of collecting real-world data.<n>We introduce Center-of-Pressure, an effective tactile representation grounded in physical principles.<n>We evaluate CoP on two blind, challenging contact-rich manipulation tasks.
  arXiv  Detail & Related papers  (2026-05-27T17:59:02Z)
• ETac: A Lightweight and Efficient Tactile Simulation Framework for Learning Dexterous Manipulation [5.35226291342953]
  We present ETac, a tactile simulation framework that models elastomeric soft-body interactions with both high fidelity and efficiency.<n>ETac employs a lightweight data-driven deformation propagation model to capture soft-body contact dynamics.<n>It supports reinforcement learning across 4,096 parallel environments, achieving a total throughput of 869 FPS.
  arXiv  Detail & Related papers  (2026-04-22T07:51:20Z)
• D-REX: Differentiable Real-to-Sim-to-Real Engine for Learning Dexterous Grasping [66.22412592525369]
  We introduce a real-to-sim-to-real engine that leverages the Gaussian Splat representations to build a differentiable engine.<n>We show that our engine achieves accurate and robust performance in mass identification across various object geometries and mass values.<n>Those optimized mass values facilitate force-aware policy learning, achieving superior and high performance in object grasping.
  arXiv  Detail & Related papers  (2026-03-01T15:32:04Z)
• Semantic-Contact Fields for Category-Level Generalizable Tactile Tool Manipulation [82.63833405368159]
  Generalizing tool manipulation requires both semantic planning and precise physical control.<n>We propose Semantic-Contact Fields (SCFields), a unified 3D representation fusing visual semantics with dense contact estimates.<n> Experiments on scraping, crayon drawing, and peeling demonstrate robust category-level generalization.
  arXiv  Detail & Related papers  (2026-02-14T16:05:08Z)
• Closing the Reality Gap: Zero-Shot Sim-to-Real Deployment for Dexterous Force-Based Grasping and Manipulation [12.509181374985936]
  Human-like dexterous hands with multiple fingers offer human-level manipulation capabilities.<n>But training control policies that can directly deploy on real hardware remains difficult due to contact-rich physics.<n>We present a practical framework that utilizes dense tactile feedback combined with joint torque sensing to regulate physical interactions.
  arXiv  Detail & Related papers  (2026-01-06T07:26:39Z)
• PICT -- A Differentiable, GPU-Accelerated Multi-Block PISO Solver for Simulation-Coupled Learning Tasks in Fluid Dynamics [62.93137406343609]
  We present our fluid simulator PICT, a differentiable pressure-implicit solver coded in PyTorch with Graphics-processing-unit (GPU) support.<n>We first verify the accuracy of both the forward simulation and our derived gradients in various established benchmarks.<n>We show that the gradients provided by our solver can be used to learn complicated turbulence models in 2D and 3D.
  arXiv  Detail & Related papers  (2025-05-22T17:55:10Z)
• DeepSimHO: Stable Pose Estimation for Hand-Object Interaction via Physics Simulation [81.11585774044848]
  We present DeepSimHO, a novel deep-learning pipeline that combines forward physics simulation and backward gradient approximation with a neural network. Our method noticeably improves the stability of the estimation and achieves superior efficiency over test-time optimization.
  arXiv  Detail & Related papers  (2023-10-11T05:34:36Z)
• Fast Aquatic Swimmer Optimization with Differentiable Projective Dynamics and Neural Network Hydrodynamic Models [23.480913364381664]
  Aquatic locomotion is a classic fluid-structure interaction (FSI) problem of interest to biologists and engineers. We present a novel, fully differentiable hybrid approach to FSI that combines a 2D numerical simulation for the deformable solid structure of the swimmer. We demonstrate the computational efficiency and differentiability of our hybrid simulator on a 2D carangiform swimmer.
  arXiv  Detail & Related papers  (2022-03-30T15:21:44Z)
• Nonprehensile Riemannian Motion Predictive Control [57.295751294224765]
  We introduce a novel Real-to-Sim reward analysis technique to reliably imagine and predict the outcome of taking possible actions for a real robotic platform. We produce a closed-loop controller to reactively push objects in a continuous action space. We observe that RMPC is robust in cluttered as well as occluded environments and outperforms the baselines.
  arXiv  Detail & Related papers  (2021-11-15T18:50:04Z)
• Elastic Tactile Simulation Towards Tactile-Visual Perception [58.44106915440858]
  We propose Elastic Interaction of Particles (EIP) for tactile simulation. EIP models the tactile sensor as a group of coordinated particles, and the elastic property is applied to regulate the deformation of particles during contact. We further propose a tactile-visual perception network that enables information fusion between tactile data and visual images.
  arXiv  Detail & Related papers  (2021-08-11T03:49:59Z)
• Optical Tactile Sim-to-Real Policy Transfer via Real-to-Sim Tactile Image Translation [21.82940445333913]
  We present a suite of simulated environments tailored towards tactile robotics and reinforcement learning. A data-driven approach enables translation of the current state of a real tactile sensor to corresponding simulated depth images. This policy is implemented within a real-time control loop on a physical robot to demonstrate zero-shot sim-to-real policy transfer.
  arXiv  Detail & Related papers  (2021-06-16T13:58:35Z)
• Point Cloud Based Reinforcement Learning for Sim-to-Real and Partial Observability in Visual Navigation [62.22058066456076]
  Reinforcement Learning (RL) represents powerful tools to solve complex robotic tasks. RL does not work directly in the real-world, which is known as the sim-to-real transfer problem. We propose a method that learns on an observation space constructed by point clouds and environment randomization.
  arXiv  Detail & Related papers  (2020-07-27T17:46:59Z)

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.