# SimShear: Sim-to-Real Shear-based Tactile Servoing

arXiv: https://arxiv.org/abs/2508.20561 · HTML: https://mlsanity.com/paper/2508.20561/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Aug 2025.

## Built on by (most cited first, 2 of 2)

- [HydroShear: Hydroelastic Shear Simulation for Tactile Sim-to-Real Reinforcement Learning](https://mlsanity.com/paper/2603.00446/) (2026)
- [Beyond Binary: Sim-to-Real Dexterous Manipulation with Physics-Grounded Contact Representation](https://mlsanity.com/paper/2605.28812/) (2026)

## Abstract (arXiv)

We present SimShear, a sim-to-real pipeline for tactile control that enables the use of shear information without explicitly modeling shear dynamics in simulation. Shear, arising from lateral movements across contact surfaces, is critical for tasks involving dynamic object interactions but remains challenging to simulate. To address this, we introduce shPix2pix, a shear-conditioned U-Net GAN that transforms simulated tactile images absent of shear, together with a vector encoding shear information, into realistic equivalents with shear deformations. This method outperforms baseline pix2pix approaches in simulating tactile images and in pose/shear prediction. We apply SimShear to two control tasks using a pair of low-cost desktop robotic arms equipped with a vision-based tactile sensor: (i) a tactile tracking task, where a follower arm tracks a surface moved by a leader arm, and (ii) a collaborative co-lifting task, where both arms jointly hold an object while the leader follows a prescribed trajectory. Our method maintains contact errors within 1 to 2 mm across varied trajectories where shear sensing is essential, validating the feasibility of sim-to-real shear modeling with rigid-body simulators and opening new directions for simulation in tactile robotics.