RoboMAN: Human-like Compliance Manipulation for Electronics Assembly via an Online Memory-Augmented Network

Rundong Wang, Yanchun Cheng, Francis E. H. Tay

Keywords: Human-like compliance control, Online memory-augmented network, Bilateral teleoperation system

TL;DR: This paper proposes RoboMAN, an online memory-augmented compliance framework that leverages a multimodal force-motion dataset from bilateral teleoperation to achieve robust, human-like compliance in precision electronics assembly tasks.

Abstract: Traditional compliance control methods face limitations in precision assembly due to delicate contact transitions and rigid parameter tuning. This paper introduces an online memory-augmented compliance learning framework RoboMAN to achieve human-like compliance control in precision electronics assembly tasks. This framework is trained on a 6-DOF force-motion dataset collected via our developed bilateral teleoperation system. Experimental evaluations on four representative electronics assembly tasks demonstrate RoboMAN’s superiority in memory efficiency (48\% GPU utilization), training speed (65.25s per epoch), inference latency (0.25s per batch), task success rates (up to 98\%), while demonstrating robust dynamic force adaptability during task execution. This work establishes a new paradigm for adaptive robotic compliance control by bridging biological compliance principles with efficient robotic execution, offering a scalable solution in precision contact-rich environments.

Submission Number: 27