Bingyi Mao

Papers

Total Citations

H-Index

About

Dr. Bingyi Mao is a roboticist whose work bridges foundational control theory and cutting-edge AI for real-time manipulation. Her research focuses on mobile robot autonomy, force-sensitive manipulation, and multimodal perception for dexterous tasks. In her early career, Dr. Mao developed a self-localization and obstacle avoidance framework for mobile robots (2009, 6 citations), addressing core challenges in autonomous navigation. She further advanced robotic control by proposing a hybrid force and position control method for manipulators using passivity backstepping neural networks (2007, 4 citations), a novel approach that integrates nonlinear control with neural learning for stable interaction with environments. Most recently, Dr. Mao introduced **MambaSlip** (2025, 2 citations), a multimodal large language model designed for real-time robotic slip detection. By leveraging LLMs for contextual reasoning, MambaSlip overcomes the limitations of traditional tactile sensing in unknown environments, enabling more accurate and adaptive decision-making during grasping. This work represents a significant step toward integrating language-based reasoning with physical manipulation. With a career spanning foundational control theory to modern AI-robotics integration, Dr. Mao’s contributions continue to shape how robots perceive and interact with the physical world.