# Bio-Inspired Proprioception for Sensorless Control of a Klann Linkage Robot Using Attention-LSTM

## TL;DR

This paper introduces a sensorless control system for a walking robot inspired by biological proprioception, using AI to predict movement from motor current data.

## Contribution

The novel approach uses motor current as interoceptive feedback and an A-LSTM model for sensorless control of a Klann linkage robot.

## Key findings

- A time-series dataset was created using motor current signals as interoceptive sensing data.
- An Attention-LSTM model successfully predicted future motor states without external sensors.
- A stable walking loop was implemented using the A-LSTM model integrated into a PI controller.

## Abstract

While walking robots possess significantpotential for various real-world applications, the reliance on high-performance sensors and complex control architectures for precise gait control remains a significant barrier to commercialization and lightweight design. To overcome these engineering limitations and lay the groundwork for a sensing paradigm adaptable to complex terrains, this study proposes an AI-based sensorless feedback control framework that incorporates the biological principles of proprioception. To this end, a walking robot leveraging the morphological intelligence of the Klann linkage was developed. We constructed a time-series dataset by defining motor current signals as ‘interoceptive sensing’ information—analogous to biological muscle feedback—and synchronizing them with absolute angular data. This dataset was used to train an Attention-LSTM (A-LSTM) model, which predicts future motor states in real-time by decoding nonlinear physical information embedded within internal current data, independent of external environmental sensors. By integrating the proposed model into a PI controller, a stable biomimetic walking loop was successfully implemented without the need for additional position sensors.