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About

Jinpeng Mi is a robotics researcher whose work centers on enabling natural, intuitive human-robot interaction through multimodal perception and learning. His primary research areas include affordance-based perception, natural language grounding, and human-robot collaboration. Mi’s most influential contribution is his 2018 paper on object affordance-based multimodal fusion for natural human-robot interaction, which has garnered 30 citations and provides a framework for robots to understand how objects can be used in context. He further advanced this line of inquiry by developing methods for intention-related natural language grounding (13 citations) and interactive grounding via scene graph parsing (12 citations), allowing robots to interpret ambiguous human instructions. Mi has also explored learning from demonstration for force-based manipulation (11 citations), gesture recognition for teleoperating robotic fish (10 citations), and hierarchical robot learning for physical collaboration (7 citations). His early work includes developing a master-slave control system for vascular intervention assistance (6 citations), demonstrating a commitment to real-world applications. Through these contributions, Mi has established himself as a researcher dedicated to making robots more perceptive, communicative, and collaborative partners in both everyday and specialized tasks.