RoboMT: Human-Like Compliance Control for Assembly via a Bilateral Robotic Teleoperation and Hybrid Mamba-Transformer Framework

Robotic compliance control is critical for delicate tasks such as electronic connector assembly, where precise force regulation and adaptability are paramount. However, traditional methods often struggle with modeling inaccuracies and sensor noise. Inspired by human adaptability in complex assembly operations, we present RoboMT, a novel framework that integrates a Mamba algorithm with a Transformer architecture to achieve human-like compliance control. By leveraging a bilateral teleoperation platform, we collect extensive real-time force/torque and motion data to form a comprehensive dataset for training. Furthermore, RoboMT incorporates an Adaptive Action Chunk module and a Temporal Fusion module to ensure smooth and robust action prediction. Experimental results across four electronic assembly tasks show that RoboMT achieves superior success rates (62–98%) over baselines (29–98%), while maintaining stable force regulation around 2.5 N, closely resembling human performance. During task transitions, RoboMT quickly stabilizes at 5 N with minimal overshoot, avoiding the large force spikes (over 24 N) seen in baselines. Additionally, RoboMT maintains an average inference speed of 55 ms per batch, balancing real-time responsiveness and control robustness. Overall, RoboMT presents a compelling pathway toward error-minimized, human-level compliance control, and generalization for real-world robotic assembly, setting a new benchmark for precision, adaptability, and robustness in robotic assembly.