Dynamic Tactile Sensing System and Soft Actor Critic Reinforcement Learning for Inclusion Characterization

TL;DR

This paper introduces a robotic tactile sensing system combined with reinforcement learning to accurately locate and characterize embedded inclusions, outperforming human operators in size estimation and enabling autonomous tissue property assessment.

Contribution

The work develops a novel Dynamic Tactile Sensing System integrated with Soft Actor Critic reinforcement learning for precise inclusion localization and characterization.

Findings

Achieved size estimation errors of 2.61% and 5.29% for inclusions.

Outperformed human operators in inclusion size estimation.

Enabled autonomous mechanical property determination.

Abstract

This paper presents the Dynamic Tactile Sensing System that utilizes robotic tactile sensing in conjunction with reinforcement learning to locate and characterize embedded inclusions. A dual arm robot is integrated with an optical Tactile Imaging Sensor that utilizes the Soft Actor Critic Algorithm to acquire tactile data based on a pixel intensity reward. A Dynamic Interrogation procedure for tactile exploration is developed that enables the robot to first localize inclusion and refine their positions for precise imaging. Experimental validation conducted on Polydimethylsiloxane phantoms demonstrates that the robot using the Tactile Soft Actor Critic Model was able to achieve size estimation errors of 2.61% and 5.29% for soft and hard inclusions compared to 7.84% and 6.87% for expert human operators. Results also show that Dynamic Tactile Sensing System was able to locate embedded inclusions and autonomously determine their mechanical properties, useful in applications such as breast tumor characterization.