# Enhancing Tactile-based Reinforcement Learning for Robotic Control

## Abstract

Effectively combining tactile sensing and reinforcement learning (RL) creates powerful new pathways for sophisticated robot manipulation. However, tactile information is not always fully exploited by neural network-based approaches in deep RL due to its unique characteristics (e.g. sparsity). Departing from conventional reliance on idealised state representations, we present a new approach to strengthen the performance of sensory-driven agents for complex manipulation tasks. We provide a novel application and analysis of tailored reconstruction and multi-step dynamics objectives that help the agent more effectively leverage its tactile observations, and propose training these objectives on a separated auxiliary memory. We find that dynamics-based objectives unlock higher-performing agents that are able to predict future contacts with high precision. Experimental results show the efficacy of our approach through a simulated robotic agent on three complex control tasks with touch and proprioception alone.

## Best PPO agent

Trained end-to-end

## Our best agent

Trained with self-supervised dynamics + an auxiliary memory

## Best PPO agent

Trained end-to-end

## Our best agent

Trained with self-supervised dynamics

## Best PPO agent

Trained end-to-en

## Our best agent

Trained with self-supervised dynamics