High-Speed, Scalable Sensor Readout for Dexterous Robotic Hands via Shift-Register Multiplexing

2026-05-02

AI Summary

This work addresses the challenge of achieving high-channel-count, high-bandwidth multimodal sensing in dexterous robotic hands, where conventional readout architectures face a trade-off between wiring complexity and sampling performance. The authors propose a scalable analog readout architecture based on a serial-in-parallel-out (SIPO) shift register, enabling flexible integration and rapid reconfiguration of heterogeneous sensor modules using only three signal lines. By leveraging shift register multiplexing, the design substantially reduces wiring complexity while supporting both high channel counts and high sampling rates without compromising accuracy. Experimental results demonstrate that the system achieves full-channel scanning at 1 kHz (peaking at 1.5 kHz), with joint angle estimation error below 1°, tactile force estimation RMSE of 0.125 N, and 93.4% accuracy in contact location classification, offering an efficient and practical sensing solution for fully perceptual dexterous hands.

Abstract

Dexterous robotic hands require high-speed multimodal sensing across many degrees of freedom, yet existing readout architectures often impose trade-offs between sensor count, wiring complexity, and sampling bandwidth. This paper presents a scalable analog sensor readout architecture based on a serial-in parallel-out (SIPO) shift-register principle. The proposed architecture supports versatile integration of heterogeneous analog-output sensors, scalable expansion using only three signal lines between sensor modules, and fast, configurable sampling. We validate the approach on a tendon-driven robotic hand integrating 16 joint sensor modules and one four-channel tactile sensor module, enabling acquisition of 20 sensor channels at a full-scan rate of 1 kHz, with stable operation up to 1.5 kHz. Joint sensor characterization showed a maximum slope absolute percentage error (APE) of 0.446% and sub-degree estimation error, indicating that the proposed readout system does not significantly degrade sensing performance. For tactile sensing, LSTM-based models achieved an RMSE of 0.125 N for force estimation and 93.4% accuracy for five-class contact-location classification, and were deployed for real-time inference at 1 kHz. System-level experiments showed that the joint sensors provide more accurate feedback than motor-based estimation during interaction, while the tactile sensor enables responsive force estimation in contact. The proposed architecture offers a practical path toward fully sensorized robotic hands for dexterous manipulation.

Problem

• dexterous robotic hands
• sensor readout
• scalability
• high-speed sensing
• wiring complexity

Innovation

• shift-register multiplexing
• scalable sensor readout
• dexterous robotic hands
• analog sensor integration
• real-time tactile sensing