Jiahang Liu

Papers

Total Citations

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About

Jiahang Liu is at the forefront of next-generation robotics, specializing in the intersection of robot learning, foundation models, and human-robot interaction (HRI). His work addresses a critical challenge: how robots can not only learn from vast datasets but also seamlessly adapt to dynamic, human-centered environments. In his highly cited survey, "Robot Learning in the Era of Foundation Models" (2024-2025), Liu provides a comprehensive roadmap for integrating large-scale pre-trained models into robotic systems, a contribution that has already garnered over 30 citations and established him as a key voice in this rapidly evolving field. Beyond surveys, Liu’s research delivers practical innovation. His novel "Human-in-the-Loop Multimodal Intention Fusion Method" introduces a flexible, adaptive framework for service robots, enabling them to interpret user intent through multiple sensory channels while dynamically adjusting to individual preferences and changing surroundings. This work directly tackles the rigidity of traditional fusion strategies, pushing HRI toward more intuitive and responsive collaboration. With a growing citation impact and a focus on bridging high-level AI with real-world interaction, Jiahang Liu is shaping a future where robots are not just intelligent, but truly collaborative partners.