# The Role of Tactile Sensing for Learning Reach and Grasp

arXiv: https://arxiv.org/abs/2502.20367 · HTML: https://mlsanity.com/paper/2502.20367/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for robot locomotion and manipulation, one of 15 directions in Deep reinforcement learning methods.
- Builds on 33 of its 48 references that are in the map.
- First posted on arXiv 27 Feb 2025.

## 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, 30 of 33)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Describing Textures in the Wild](https://mlsanity.com/paper/1311.3618/) (2013)
- [Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning](https://mlsanity.com/paper/2108.10470/) (2021)
- [QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation](https://mlsanity.com/paper/1806.10293/) (2018)
- [Google Scanned Objects: A High-Quality Dataset of 3D Scanned Household Items](https://mlsanity.com/paper/2204.11918/) (2022)
- [FoundationPose: Unified 6D Pose Estimation and Tracking of Novel Objects](https://mlsanity.com/paper/2312.08344/) (2023)
- [Maximum a Posteriori Policy Optimisation](https://mlsanity.com/paper/1806.06920/) (2018)
- [Stochastic Latent Actor-Critic: Deep Reinforcement Learning with a Latent Variable Model](https://mlsanity.com/paper/1907.00953/) (2019)
- [Robust Reinforcement Learning for Continuous Control with Model Misspecification](https://mlsanity.com/paper/1906.07516/) (2019)
- [GraspNeRF: Multiview-based 6-DoF Grasp Detection for Transparent and Specular Objects Using Generalizable NeRF](https://mlsanity.com/paper/2210.06575/) (2022)
- [Visuo-Tactile Transformers for Manipulation](https://mlsanity.com/paper/2210.00121/) (2022)
- [GelSlim3.0: High-Resolution Measurement of Shape, Force and Slip in a Compact Tactile-Sensing Finger](https://mlsanity.com/paper/2103.12269/) (2021)
- [ReSkin: versatile, replaceable, lasting tactile skins](https://mlsanity.com/paper/2111.00071/) (2021)
- [Deep Reinforcement Learning for Vision-Based Robotic Grasping: A Simulated Comparative Evaluation of Off-Policy Methods](https://mlsanity.com/paper/1802.10264/) (2018)
- [DEP-RL: Embodied Exploration for Reinforcement Learning in Overactuated and Musculoskeletal Systems](https://mlsanity.com/paper/2206.00484/) (2022)
- [Dextrous Tactile In-Hand Manipulation Using a Modular Reinforcement Learning Architecture](https://mlsanity.com/paper/2303.04705/) (2023)
- [TactileGCN: A Graph Convolutional Network for Predicting Grasp Stability with Tactile Sensors](https://mlsanity.com/paper/1901.06181/) (2019)
- [GelTip: A Finger-shaped Optical Tactile Sensor for Robotic Manipulation](https://mlsanity.com/paper/2008.05404/) (2020)
- [MAT: Multi-Fingered Adaptive Tactile Grasping via Deep Reinforcement Learning](https://mlsanity.com/paper/1909.04787/) (2019)
- [DexTouch: Learning to Seek and Manipulate Objects with Tactile Dexterity](https://mlsanity.com/paper/2401.12496/) (2024)
- [Sim-to-Real Transfer for Robotic Manipulation with Tactile Sensory](https://mlsanity.com/paper/2103.00410/) (2021)
- [TacGNN:Learning Tactile-based In-hand Manipulation with a Blind Robot](https://mlsanity.com/paper/2304.00736/) (2023)
- [Improved Learning of Robot Manipulation Tasks via Tactile Intrinsic Motivation](https://mlsanity.com/paper/2102.11051/) (2021)
- [A soft thumb-sized vision-based sensor with accurate all-round force perception](https://mlsanity.com/paper/2111.05934/) (2021)
- [A Sensorized Multicurved Robot Finger with Data-driven Touch Sensing via Overlapping Light Signals](https://mlsanity.com/paper/2004.00685/) (2020)
- [Soft, Round, High Resolution Tactile Fingertip Sensors for Dexterous Robotic Manipulation](https://mlsanity.com/paper/2005.09068/) (2020)
- [DenseTact 2.0: Optical Tactile Sensor for Shape and Force Reconstruction](https://mlsanity.com/paper/2209.10122/) (2022)
- [GraspARL: Dynamic Grasping via Adversarial Reinforcement Learning](https://mlsanity.com/paper/2203.02119/) (2022)
- [GelSight360: An Omnidirectional Camera-Based Tactile Sensor for Dexterous Robotic Manipulation](https://mlsanity.com/paper/2304.04268/) (2023)

## Abstract (arXiv)

Stable and robust robotic grasping is essential for current and future robot applications. In recent works, the use of large datasets and supervised learning has enhanced speed and precision in antipodal grasping. However, these methods struggle with perception and calibration errors due to large planning horizons. To obtain more robust and reactive grasping motions, leveraging reinforcement learning combined with tactile sensing is a promising direction. Yet, there is no systematic evaluation of how the complexity of force-based tactile sensing affects the learning behavior for grasping tasks. This paper compares various tactile and environmental setups using two model-free reinforcement learning approaches for antipodal grasping. Our findings suggest that under imperfect visual perception, various tactile features improve learning outcomes, while complex tactile inputs complicate training.