Tactile-Based Human Intent Recognition for Robot Assistive Navigation

Abstract-- Robot assistive navigation (RAN) is critical for enhancing the mobility and independence of the growing population of mobility-impaired individuals. However, existing systems often rely on interfaces that fail to replicate the intuitive and efficient physical communication observed between a person and a human caregiver, limiting their effectiveness. In this paper, we introduce T ac-Nav, a RAN system that leverages a cylindrical tactile skin mounted on a Stretch 3 mobile manipulator to provide a more natural and efficient interface for human navigational intent recognition. T o robustly classify the tactile data, we developed the Cylindrical Kernel Support V ector Machine (CK-SVM), an algorithm that explicitly models the sensor's cylindrical geometry and is consequently robust to the natural rotational shifts present in a user's grasp. Comprehensive experiments were conducted to demonstrate the effectiveness of our classification algorithm and the overall system. Results show that CK-SVM achieved superior classification accuracy on both simulated (97.1%) and real-world (90.8%) datasets compared to four baseline models. Furthermore, a pilot study confirmed that users more preferred the T ac-Nav tactile interface over conventional joystick and voice-based controls. I. INTRODUCTION Robot assistive navigation (RAN) - the task of a robot providing physical support while people moving from one place to another - is of critical importance for people with mobility impairments [1]. In the U.S., 12.2% of adults live with a mobility disability, and the aging population suggests an increasing need for navigation assistance [2], [3].