Estimating Properties of Solid Particles Inside Container Using Touch Sensing

TL;DR

This paper presents a tactile sensing approach for robots to estimate properties of solid particles inside containers, such as mass, volume, size, and shape, using a sequence of interactions and specialized sensors, enabling better manipulation in daily and industrial tasks.

Contribution

It introduces a novel tactile sensing method combining force, vibration, and topple features to estimate multiple particle properties with high accuracy, including generalization to unseen particles.

Findings

Mass estimation error of 1.8 g

Volume estimation error of 6.1 ml

75.6% accuracy in shape classification

Abstract

Solid particles, such as rice and coffee beans, are commonly stored in containers and are ubiquitous in our daily lives. Understanding those particles' properties could help us make later decisions or perform later manipulation tasks such as pouring. Humans typically interact with the containers to get an understanding of the particles inside them, but it is still a challenge for robots to achieve that. This work utilizes tactile sensing to estimate multiple properties of solid particles enclosed in the container, specifically, content mass, content volume, particle size, and particle shape. We design a sequence of robot actions to interact with the container. Based on physical understanding, we extract static force/torque value from the F/T sensor, vibration-related features and topple-related features from the newly designed high-speed GelSight tactile sensor to estimate those four particle properties. We test our method on $37$ very different daily particles, including powder, rice, beans, tablets, etc. Experiments show that our approach is able to estimate content mass with an error of $1.8$ g, content volume with an error of $6.1$ ml, particle size with an error of $1.1$ mm, and achieves an accuracy of $75.6$% for particle shape estimation. In addition, our method can generalize to unseen particles with unknown volumes. By estimating these particle properties, our method can help robots to better perceive the granular media and help with different manipulation tasks in daily life and industry.