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

arXiv: https://arxiv.org/abs/2603.00446 · HTML: https://mlsanity.com/paper/2603.00446/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Placed in RL for robot locomotion and manipulation, one of 15 directions in Deep reinforcement learning methods.
- Builds on 25 of its 28 references that are in the map.
- First posted on arXiv 28 Feb 2026.

## Placed in

- Deep reinforcement learning methods / [RL for robot locomotion and manipulation](https://mlsanity.com/deeprl/rl-for-robot-locomotion-and-manipulation/)

## Builds on (most cited first, 25 of 25)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning](https://mlsanity.com/paper/2108.10470/) (2021)
- [RMA: Rapid Motor Adaptation for Legged Robots](https://mlsanity.com/paper/2107.04034/) (2021)
- [DiffTaichi: Differentiable Programming for Physical Simulation](https://mlsanity.com/paper/1910.00935/) (2019)
- [Reinforcement Learning for Robust Parameterized Locomotion Control of Bipedal Robots](https://mlsanity.com/paper/2103.14295/) (2021)
- [TACTO: A Fast, Flexible, and Open-source Simulator for High-Resolution Vision-based Tactile Sensors](https://mlsanity.com/paper/2012.08456/) (2020)
- [Taxim: An Example-based Simulation Model for GelSight Tactile Sensors](https://mlsanity.com/paper/2109.04027/) (2021)
- [Learning Quadruped Locomotion Using Differentiable Simulation](https://mlsanity.com/paper/2403.14864/) (2024)
- [Sim-to-Real for Robotic Tactile Sensing via Physics-Based Simulation and Learned Latent Projections](https://mlsanity.com/paper/2103.16747/) (2021)
- [TacSL: A Library for Visuotactile Sensor Simulation and Learning](https://mlsanity.com/paper/2408.06506/) (2024)
- [DIFFTACTILE: A Physics-based Differentiable Tactile Simulator for Contact-rich Robotic Manipulation](https://mlsanity.com/paper/2403.08716/) (2024)
- [Generation of GelSight Tactile Images for Sim2Real Learning](https://mlsanity.com/paper/2101.07169/) (2021)
- [Learning In-Hand Translation Using Tactile Skin With Shear and Normal Force Sensing](https://mlsanity.com/paper/2407.07885/) (2024)
- [TacEx: GelSight Tactile Simulation in Isaac Sim -- Combining Soft-Body and Visuotactile Simulators](https://mlsanity.com/paper/2411.04776/) (2024)
- [A pressure field model for fast, robust approximation of net contact force and moment between nominally rigid objects](https://mlsanity.com/paper/1904.11433/) (2019)
- [Sim-to-Real Transfer for Optical Tactile Sensing](https://mlsanity.com/paper/2004.00136/) (2020)
- [FOTS: A Fast Optical Tactile Simulator for Sim2Real Learning of Tactile-motor Robot Manipulation Skills](https://mlsanity.com/paper/2404.19217/) (2024)
- [Interpreting and Predicting Tactile Signals via a Physics-Based and Data-Driven Framework](https://mlsanity.com/paper/2006.03777/) (2020)
- [Velocity Level Approximation of Pressure Field Contact Patches](https://mlsanity.com/paper/2110.04157/) (2021)
- [Beyond Flat GelSight Sensors: Simulation of Optical Tactile Sensors of Complex Morphologies for Sim2Real Learning](https://mlsanity.com/paper/2305.12605/) (2023)
- [ViTaSCOPE: Visuo-tactile Implicit Representation for In-hand Pose and Extrinsic Contact Estimation](https://mlsanity.com/paper/2506.12239/) (2025)
- [LocoMamba: Vision-Driven Locomotion via End-to-End Deep Reinforcement Learning with Mamba](https://mlsanity.com/paper/2508.11849/) (2025)
- [SimShear: Sim-to-Real Shear-based Tactile Servoing](https://mlsanity.com/paper/2508.20561/) (2025)
- [Shear-based Grasp Control for Multi-fingered Underactuated Tactile Robotic Hands](https://mlsanity.com/paper/2503.17501/) (2025)
- [Hydrosoft: Non-Holonomic Hydroelastic Models for Compliant Tactile Manipulation](https://mlsanity.com/paper/2509.13126/) (2025)

## Built on by (most cited first, 4 of 4)

- [ContactWorld: What Representations Matter for Vision-Tactile Latent World Models in Contact-Rich Manipulation](https://mlsanity.com/paper/2606.13877/) (2026)
- [ADEPT: Accelerating Dexterity via Pre-Training and Post-Training using Reinforcement Learning](https://mlsanity.com/paper/2608.19182/) (2026)
- [Learning Physical Interaction: A Survey of Tactile- and Force-aware Robot Learning](https://mlsanity.com/paper/2608.07558/) (2026)
- [Beyond Binary: Sim-to-Real Dexterous Manipulation with Physics-Grounded Contact Representation](https://mlsanity.com/paper/2605.28812/) (2026)

## Abstract (arXiv)

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%).