Fluidically Innervated Lattices Make Versatile and Durable Tactile Sensors

TL;DR

This paper presents a simple, scalable, and durable tactile sensor for robots, using a 3D-printed elastomer lattice with embedded air channels that detect pressure changes for effective manipulation and exploration.

Contribution

It introduces a novel fluidic innervation technique for tactile sensing using a single-material 3D-printed lattice with embedded air channels, enhancing robustness and scalability.

Findings

Sensor response characterized and modeled

Neural network accurately predicts contact parameters

Demonstrated durability under high impact and cyclic loading

Abstract

Tactile sensing plays a fundamental role in enabling robots to navigate dynamic and unstructured environments, particularly in applications such as delicate object manipulation, surface exploration, and human-robot interaction. In this paper, we introduce a passive soft robotic fingertip with integrated tactile sensing, fabricated using a 3D-printed elastomer lattice with embedded air channels. This sensorization approach, termed fluidic innervation, transforms the lattice into a tactile sensor by detecting pressure changes within sealed air channels, providing a simple yet robust solution to tactile sensing in robotics. Unlike conventional methods that rely on complex materials or designs, fluidic innervation offers a simple, scalable, single-material fabrication process. We characterize the sensors' response, develop a geometric model to estimate tip displacement, and train a neural network to accurately predict contact location and contact force. Additionally, we integrate the fingertip with an admittance controller to emulate spring-like behavior, demonstrate its capability for environment exploration through tactile feedback, and validate its durability under high impact and cyclic loading conditions. This tactile sensing technique offers advantages in terms of simplicity, adaptability, and durability and opens up new opportunities for versatile robotic manipulation.