# Surface Following using Deep Reinforcement Learning and a GelSightTactile Sensor

arXiv: https://arxiv.org/abs/1912.00745 · HTML: https://mlsanity.com/paper/1912.00745/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Dec 2019.

## Built on by (most cited first, 3 of 3)

- [Imitation Learning for Variable Speed Contact Motion for Operation up to Control Bandwidth](https://mlsanity.com/paper/2102.10283/) (2021)
- [Visual-Tactile Multimodality for Following Deformable Linear Objects Using Reinforcement Learning](https://mlsanity.com/paper/2204.00117/) (2022)
- [Tactile Image-to-Image Disentanglement of Contact Geometry from Motion-Induced Shear](https://mlsanity.com/paper/2109.03615/) (2021)

## Abstract (arXiv)

Tactile sensors can provide detailed contact in-formation that can facilitate robots to perform dexterous, in-hand manipulation tasks. One of the primitive but important tasks is surface following that is a key feature for robots while exploring unknown environments or workspace of inaccurate modeling. In this paper, we propose a novel end-to-end learning strategy, by directly mapping the raw tactile data acquired from a GelSight tactile sensor to the motion of the robot end-effector.Experiments on a KUKA youBot platform equipped with theGelSight sensor show that 80% of the actions generated by a fully trained SFDQN model are proper surface following actions; the autonomous surface following test also indicates that the proposed solution works well on a test surface.