# Towards Transferring Tactile-based Continuous Force Control Policies from Simulation to Robot

arXiv: https://arxiv.org/abs/2311.07245 · HTML: https://mlsanity.com/paper/2311.07245/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 38 references that are in the map.
- First posted on arXiv 13 Nov 2023.

## Builds on (most cited first, 22 of 22)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Solving Rubik's Cube with a Robot Hand](https://mlsanity.com/paper/1910.07113/) (2019)
- [Sim-to-Real Transfer of Robotic Control with Dynamics Randomization](https://mlsanity.com/paper/1710.06537/) (2017)
- [Sim-to-Real Transfer in Deep Reinforcement Learning for Robotics: a Survey](https://mlsanity.com/paper/2009.13303/) (2020)
- [Curriculum Learning for Reinforcement Learning Domains: A Framework and Survey](https://mlsanity.com/paper/2003.04960/) (2020)
- [DIGIT: A Novel Design for a Low-Cost Compact High-Resolution Tactile Sensor with Application to In-Hand Manipulation](https://mlsanity.com/paper/2005.14679/) (2020)
- [Regularizing Action Policies for Smooth Control with Reinforcement Learning](https://mlsanity.com/paper/2012.06644/) (2020)
- [Reinforcement Learning on Variable Impedance Controller for High-Precision Robotic Assembly](https://mlsanity.com/paper/1903.01066/) (2019)
- [Learning Curriculum Policies for Reinforcement Learning](https://mlsanity.com/paper/1812.00285/) (2018)
- [Sim-to-Real for Robotic Tactile Sensing via Physics-Based Simulation and Learned Latent Projections](https://mlsanity.com/paper/2103.16747/) (2021)
- [Tactile Sim-to-Real Policy Transfer via Real-to-Sim Image Translation](https://mlsanity.com/paper/2106.08796/) (2021)
- [Tactile Gym 2.0: Sim-to-real Deep Reinforcement Learning for Comparing Low-cost High-Resolution Robot Touch](https://mlsanity.com/paper/2207.10763/) (2022)
- [Zero-shot sim-to-real transfer of tactile control policies for aggressive swing-up manipulation](https://mlsanity.com/paper/2101.02680/) (2021)
- [Leveraging Contact Forces for Learning to Grasp](https://mlsanity.com/paper/1809.07004/) (2018)
- [MAT: Multi-Fingered Adaptive Tactile Grasping via Deep Reinforcement Learning](https://mlsanity.com/paper/1909.04787/) (2019)
- [Sim-to-Real Transfer for Robotic Manipulation with Tactile Sensory](https://mlsanity.com/paper/2103.00410/) (2021)
- [DigiTac: A DIGIT-TacTip Hybrid Tactile Sensor for Comparing Low-Cost High-Resolution Robot Touch](https://mlsanity.com/paper/2206.13657/) (2022)
- [Sim-to-Real Transfer for Optical Tactile Sensing](https://mlsanity.com/paper/2004.00136/) (2020)
- [Learning the sense of touch in simulation: a sim-to-real strategy for vision-based tactile sensing](https://mlsanity.com/paper/2003.02640/) (2020)
- [Interpreting and Predicting Tactile Signals for the SynTouch BioTac](https://mlsanity.com/paper/2101.05452/) (2021)
- [Placing by Touching: An empirical study on the importance of tactile sensing for precise object placing](https://mlsanity.com/paper/2210.02054/) (2022)
- [A Versatile Door Opening System with Mobile Manipulator through Adaptive Position-Force Control and Reinforcement Learning](https://mlsanity.com/paper/2307.04422/) (2023)

## Built on by (most cited first, 1 of 1)

- [Blind Dexterous Grasping via Real2Sim2Real Tactile Policy Learning](https://mlsanity.com/paper/2606.11767/) (2026)

## Abstract (arXiv)

The advent of tactile sensors in robotics has sparked many ideas on how robots can leverage direct contact measurements of their environment interactions to improve manipulation tasks. An important line of research in this regard is that of grasp force control, which aims to manipulate objects safely by limiting the amount of force exerted on the object. While prior works have either hand-modeled their force controllers, employed model-based approaches, or have not shown sim-to-real transfer, we propose a model-free deep reinforcement learning approach trained in simulation and then transferred to the robot without further fine-tuning. We therefore present a simulation environment that produces realistic normal forces, which we use to train continuous force control policies. An evaluation in which we compare against a baseline and perform an ablation study shows that our approach outperforms the hand-modeled baseline and that our proposed inductive bias and domain randomization facilitate sim-to-real transfer. Code, models, and supplementary videos are available on https://sites.google.com/view/rl-force-ctrl