Contactless Human–Robot Interaction for Adaptive Intelligent Control

Abstract

Human–Robot Interaction (HRI) is a rapidly evolving research area focused on enabling intuitive, efficient, and reliable communication between humans and robotic systems. Unlike conventional robotic control interfaces, we propose a contactless control system that leverages American Sign Language (ASL) as a natural, non-intrusive, and accessible modality for remote robot operation. Our framework integrates computer vision, gesture recognition, and machine learning to achieve accurate real-time interpretation of ASL gestures and their direct translation into robotic commands. To ensure robust gesture understanding, the system employs a Spatial–Temporal Network that captures both the spatial relationships of hand and body positions as well as the temporal dynamics of gesture sequences. Recognized gestures are mapped to precise control commands that drive the motors of a robotic car, enabling responsive and accurate navigation based solely on sign-based inputs. Extensive experiments demonstrate that the proposed system achieves high gesture recognition accuracy across multiple conditions, including variations in speed, angle, and handedness, while maintaining safe and reliable robot operation. Tasks executed using this framework are performed consistently and accurately, validating its effectiveness. This paper highlights the potential of ASL-based, contactless robotic control to enhance accessibility, safety, and intuitiveness in human–robot interaction, paving the way for more natural and inclusive interfaces in autonomous systems.