# TIAGo RL: Simulated Reinforcement Learning Environments with Tactile Data for Mobile Robots

arXiv: https://arxiv.org/abs/2311.07260 · HTML: https://mlsanity.com/paper/2311.07260/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 10 references that are in the map.
- First posted on arXiv 13 Nov 2023.

## Builds on (most cited first, 5 of 5)

- [Addressing Function Approximation Error in Actor-Critic Methods](https://mlsanity.com/paper/1802.09477/) (2018)
- [Learning Gentle Object Manipulation with Curiosity-Driven Deep Reinforcement Learning](https://mlsanity.com/paper/1903.08542/) (2019)
- [MAT: Multi-Fingered Adaptive Tactile Grasping via Deep Reinforcement Learning](https://mlsanity.com/paper/1909.04787/) (2019)
- [Modular Architecture for StarCraft II with Deep Reinforcement Learning](https://mlsanity.com/paper/1811.03555/) (2018)
- [Deep Reinforcement Learning for Tactile Robotics: Learning to Type on a Braille Keyboard](https://mlsanity.com/paper/2008.02646/) (2020)

## Abstract (arXiv)

Tactile information is important for robust performance in robotic tasks that involve physical interaction, such as object manipulation. However, with more data included in the reasoning and control process, modeling behavior becomes increasingly difficult. Deep Reinforcement Learning (DRL) produced promising results for learning complex behavior in various domains, including tactile-based manipulation in robotics. In this work, we present our open-source reinforcement learning environments for the TIAGo service robot. They produce tactile sensor measurements that resemble those of a real sensorised gripper for TIAGo, encouraging research in transfer learning of DRL policies. Lastly, we show preliminary training results of a learned force control policy and compare it to a classical PI controller.