Robotic Perception of Object Properties using Tactile Sensing

TL;DR

This paper reviews recent advances in tactile sensing for robotic grasping, focusing on perception of object shape, pose, material, and stability, and demonstrates how tactile data can enhance visual perception for tasks like crack reconstruction.

Contribution

It provides a comprehensive overview of tactile perception of object properties and introduces a vision-guided tactile method for precise crack shape reconstruction in robotics.

Findings

Tactile sensing improves understanding of object shape, pose, and material.

Combining vision and tactile sensing enhances crack reconstruction accuracy.

Proposed method reduces mean distance error from 0.82 mm to 0.24 mm.

Abstract

The sense of touch plays a key role in enabling humans to understand and interact with surrounding environments. For robots, tactile sensing is also irreplaceable. While interacting with objects, tactile sensing provides useful information for the robot to understand the object, such as distributed pressure, temperature, vibrations and texture. During robot grasping, vision is often occluded by its end-effectors, whereas tactile sensing can measure areas that are not accessible by vision. In the past decades, a number of tactile sensors have been developed for robots and used for different robotic tasks. In this chapter, we focus on the use of tactile sensing for robotic grasping and investigate the recent trends in tactile perception of object properties. We first discuss works on tactile perception of three important object properties in grasping, i.e., shape, pose and material properties. We then review the recent development in grasping stability prediction with tactile sensing. Among these works, we identify the requirement for coordinating vision and tactile sensing in the robotic grasping. To demonstrate the use of tactile sensing to improve the visual perception, our recent development of vision-guided tactile perception for crack reconstruction is presented. In the proposed framework, the large receptive field of camera vision is first leveraged to achieve a quick search of candidate regions containing cracks, a high-resolution optical tactile sensor is then used to examine these candidate regions and reconstruct a refined crack shape. The experiments show that our proposed method can achieve a significant reduction of mean distance error from 0.82 mm to 0.24 mm for crack reconstruction. Finally, we conclude this chapter with a discussion of open issues and future directions for applying tactile sensing in robotic tasks.