Minrui Meng

Papers

Total Citations

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About

Minrui Meng is a researcher specializing in adaptive control systems and bio-inspired robotics, with a particular focus on tendon-driven mechanisms and neural network-based torque control. Their most-cited work, "Adaptive SNN Torque Control for Tendon-Driven Fingers" (2017), introduces a spiking neural network (SNN) framework that enables precise, adaptive torque regulation in robotic fingers—a critical advancement for dexterous manipulation and prosthetics. This contribution bridges computational neuroscience and robotics, demonstrating how biologically plausible SNNs can achieve robust control in nonlinear, underactuated systems. Though early in their career, Meng’s work has garnered attention for its novel integration of adaptive learning with tendon-driven architectures, offering a pathway toward more natural and efficient robotic hands. Their research holds promise for applications in assistive devices, industrial automation, and human-robot interaction, where adaptive, low-latency control is essential. Meng’s ongoing efforts continue to push the boundaries of neural control in robotics, positioning them as an emerging voice in the field of intelligent actuation and bio-inspired design.