Integration and Continual Learning-Based Modeling of a Soft Robotic Sensor for Social Robot Proprioception

Abstract

This paper presents an embedded soft sensor for proprioceptive feedback in a soft continuum actuator (SCA) forming the neck of the social robot HARU. The sensor is fabricated in a single-step multi-material additive manufacturing process, co-extruding conductive and non-conductive thermoplastic polyurethane to form an integrated structure. Several sensor geometries are evaluated, with a gauge-type configuration selected based on linearity and repeatability criteria. The design is embedded in a cross-configuration to measure the actuator’s two dominant degrees of freedom, pitch and roll. Sensor signals are mapped to angle estimates using linear regression, a static neural network, and a continual-learning framework that updates parameters online. Experiments involving predefined trajectories, randomized motions, and repeated test cycles show that the continual-learning model achieves R 2 > 0.97 and mean absolute errors below 1 degree, consistently improving upon the baseline models. The results demonstrate the feasibility of directly embedding 3D-printed soft sensors into functional actuators and highlight the role of adaptive learning in supporting long-term soft robotic proprioception.