Self Capacitive Tactile Sensor System designed for Companion Robots

2026-06-23

AI Summary

This study addresses the challenges of complexity, wiring overhead, and high cost in existing tactile sensing systems, which hinder low-latency, full-body haptic perception in companion robots. The authors propose a streamlined and scalable architecture based on self-capacitance principles, requiring only a single layer of conductive fabric and conductive threads—eliminating the need for intricate electrode patterning. By integrating flexible printed circuits with an FPGA-based embedded platform, they deploy a lightweight decision tree classifier to enable low-power, low-latency edge inference. The resulting system successfully implements a 100-sensor-point flexible array capable of accurately distinguishing four interaction types—light touch, slow tap, fast tap, and strike—at sampling rates of at least 100 Hz, thereby fulfilling the whole-body tactile sensing requirements of the HIRO-chan companion robot.

Abstract

Tactile sensing is essential for humanoid robots to achieve safe physical interaction, dexterous manipulation, and truly human-like responsiveness. However, the design of such systems remains challenging. Conventional approaches often suffer from complex multilayer structures, intricate wiring, high cost, and poor scalability, making it difficult to realize full-body tactile sensing with real-time, low-latency detection while maintaining minimal computational load on the robot's main processor. In this work, we present a simple, scalable and hardware friendly tactile sensing system for a companion humanoid robot based on the self-capacitance principle. The proposed sensor system employs a single conductive fabric layer with a conductive fabric wire architecture and does not require intricate electrode patterning. Scalability was demonstrated by fabricating a 100-point sensor array on a flexible printed circuit (FPC). Evaluation across sampling frequencies showed that 10 Hz is insufficient and misses transient events, whereas 100 Hz and 1000 Hz reliably capture and clearly distinguish all interaction types: gentle touch, slow tapping, fast tapping, and hitting. A decision-tree classifier was implemented directly on the FPGA, offloading real-time inference from the Raspberry Pi 4 with minimal latency and negligible power overhead. This design fully meets the tactile sensing requirements of the HIRO-chan robot and is well-suited for full-body tactile sensing in HIRO-chan and other companion robots.

Problem

• tactile sensing
• companion robots
• scalability
• real-time detection
• low-latency

Innovation

• self-capacitance
• scalable tactile sensing
• conductive fabric
• FPGA-based real-time classification
• companion robot