Recent Progress in Tactile Sensing and Machine Learning for Texture Perception in Humanoid Robotics

Abstract

Humanoid robots have garnered substantial attention recently in both academia and industry. These robots are becoming increasingly sophisticated and intelligent, as seen in health care, education, customer service, logistics, security, space exploration, and so forth. Central to these technological advancements is tactile perception, a crucial modality through which humanoid robots exchange information with their external environment, thereby facilitating human‐like behaviors such as object recognition and dexterous manipulation. Texture perception is particularly vital for these tasks, as the surface morphology of objects significantly influences recognition and manipulation abilities. This review addresses the recent progress in tactile sensing and machine learning for texture perception in humanoid robots. We first examine the design and working principles of tactile sensors employed in texture perception, differentiating between touch‐based and sliding‐based approaches. Subsequently, we delve into the machine learning algorithms implemented for texture perception using these tactile sensors. Finally, we discuss the challenges and future opportunities in this evolving field. This review aims to provide insights into the state‐of‐the‐art developments and foster advancements in tactile sensing and machine learning for texture perception in humanoid robotics.