Real-Time Neuroadaptive Control with Tactile Calibration for Physical Human–Robot Interaction

Abstract

We present a neuroadaptive control framework applied to a tactile sensing interface for real-time, human-guided physical interaction with a robotic arm. The framework employs a dual-loop architecture consisting of an inner-loop neuroadaptive controller that compensates for nonlinear robot dynamics and an outer-loop ARMA–RLS tactile mapping that converts tactile sensor voltages into planar end-effector displacement commands. Four piezoresistive tactile sensors mounted on the robot end-effector are calibrated individually using autoregressive moving-average (ARMA) models updated through recursive least squares (RLS). The proposed tactile interface does not estimate an absolute Cartesian force/torque wrench; instead, it learns a user- and sensor-specific voltage-to-motion command mapping for planar guidance. To evaluate robustness to user variability, 28 participants completed the calibration experiments, producing 112 user- and sensor-specific calibration models. The calibration procedure achieved millimeter-level displacement-prediction accuracy, with a mean RMSE of approximately 2.70 mm across participants. After calibration, participants used the tactile interface to guide the robot along a predefined figure-eight trajectory. The average nearest-path tracking error decreased from 11.07±5.45 mm in the initial trial to 8.41±3.48 mm in the final trial, indicating improved tactile-guided path following after repeated exposure to the interface. During these experiments, the inner neuroadaptive controller maintained bounded joint-space tracking errors. Overall, the proposed calibration and control framework provides a low-cost physical interface for planar human-guided robot motion without requiring a wrist-mounted force/torque sensor.