Autonomous Door-Opening with a Dual-Arm Robot

This paper presents the development and evaluation of specialized perception and manipulation methodologies for a dual-arm robot system. The goal of this research is to enhance the adaptability and performance of this system for intricate real-world bimanual tasks. The proposed approach is validated through a key real-world task - autonomous door-opening. This particular task provides an excellent test bed, as it requires robust perception, coordinated manipulation, and context-aware decision-making in a human-centric environment. To tackle these challenges, we employ methods such as camera-based object recognition and localization, task-specific motion planning algorithms, and integrated force feedback mechanisms. The proposed strategies for door-opening have been successfully implemented, leading to improvements in the overall performance, robustness, and adaptability of the dual-arm robot system under varied conditions.