The impact of tactile sensor configurations on grasp learning efficiency -- a comparative evaluation in simulation

TL;DR

This study evaluates six tactile sensor configurations in simulation to determine their impact on grasp learning efficiency, identifying the most effective layout for robotic hands and prosthetics.

Contribution

It provides a comparative analysis of tactile sensor configurations using simulation, highlighting the most effective layout for improving grasp learning.

Findings

One configuration consistently outperforms others in grasp learning efficiency.

Sensor layout impacts reinforcement learning success in robotic grasping.

Results are robust across different simulation setups.

Abstract

Tactile sensors are breaking into the field of robotics to provide direct information related to contact surfaces, including contact events, slip events and even texture identification. These events are especially important for robotic hand designs, including prosthetics, as they can greatly improve grasp stability. Most presently published robotic hand designs, however, implement them in vastly different densities and layouts on the hand surface, often reserving the majority of the available space. We used simulations to evaluate 6 different tactile sensor configurations with different densities and layouts, based on their impact on reinforcement learning. Our two-setup system allows for robust results that are not dependent on the use of a given physics simulator, robotic hand model or machine learning algorithm. Our results show setup-specific, as well as generalized effects across the 6 sensorized simulations, and we identify one configuration as consistently yielding the best performance across both setups. These results could help future research aimed at robotic hand designs, including prostheses.