Tactile sensing improves robots ability to manipulate objects
Tactile-Based Robotic Manipulation: From Mechanics-Driven to Data-Driven Perspectives
Robotics
Summary
Robots need to feel what they touch to handle objects carefully and skillfully. This paper explains how the ways machines understand touch have changed over time, from using fixed rules about contact forces to using lots of data to learn by experience. It reviews early methods based on physics and mechanics, and newer methods that use data-driven learning like imitation and trial-and-error. The authors also talk about current challenges and what might come next to make touch-sensitive robots more reliable and useful.
What this means in practice
- •For robotics engineers: Design robots that can handle objects delicately by integrating tactile sensing with learned control strategies for better performance in complex tasks.
- •For automation system developers: Improve industrial robots' adaptability by applying data-driven tactile models that enable safer and more precise object manipulation on factory lines.
A survey. It maps existing work.
Authors
Jingyi Xiang, Yixuan Wang, Wenzhen Yuan
Abstract
Tactile sensing provides robots with direct access to physical interactions at the contact interface, making it a critical modality for robust and dexterous manipulation. Over the past several decades, tactile-based manipulation research has evolved from mechanics-driven approaches, which explicitly interpret tactile signals through contact mechanics and task-specific reasoning, to data-driven approaches that learn tactile representations and manipulation policies directly from data. This paper reviews the development of tactile-based manipulation from these two complementary perspectives. We first introduce the fundamentals of tactile sensing, and then review, from a mechanics-driven perspective, how those signals form manipulation-relevant state representations and subsequently influence the robot action. We then examine the recent advances in data-driven tactile manipulation, covering imitation learning, reinforcement learning, vision-language-action models, and predictive models. Finally, we discuss open challenges and future opportunities toward scalable, generalizable, and robust tactile-enabled robotic systems.