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Research Vision
Research Vision
My vision is to develop collective physical intelligence inspired by collective behaviours observed in nature and human interactions, significantly contributing to foundational theories and practical robotic systems. This collective intelligence will facilitate cooperation among humans, robots, and nature, dramatically extending human capabilities, enhancing skills, and transforming applications across various domains such as everyday life, industrial environments, healthcare facilities, environmental monitoring, and beyond.
My long-term goal is to harness insights from biological swarm intelligence and human behaviour, enabling robotic systems to seamlessly integrate into everyday environments, assisting people at home, monitoring environment, enhancing workplace efficiency, supporting patient care in hospitals, and collaborating with operators in manufacturing contexts.
The technical approach involves three main components. First, I will continue developing models and tools to deepen the understanding of the intrinsic principles underlying collective behaviours observed in nature. Second, inspired by these natural systems, I will formulate novel theories and methodologies to enhance structured machine learning methods capable of handling spatiotemporal reasoning and memory integration in dynamic physical interactions. Lastly, I will explore practical applications of these nature- and human-inspired theories and methods, focusing on enhancing interactions among humans, robots, and natural environments, thereby improving operations in industrial settings, healthcare facilities, and daily life scenarios.
Publications
Publications
Research Highlights
Research Highlights
Self-supervised learning
Self-supervised learning
Yin, L., Rus, D., Rosman, G., Meireles, O.(2025). Surgical Foundation Model Leveraging Compression and Entropy Maximization for Image-Guided Surgical Assistance. arXiv preprint arXiv:2506.01980.
Artificial Intelligence in Surgery
Artificial Intelligence in Surgery
Yin, L., Ban, Y., Eckhoff, J., Meireles, O., Rus, D., & Rosman, G. (2025). Hypergraph-Transformer (HGT) for Interactive Event Prediction in Laparoscopic and Robotic Surgery, IEEE International Conference on Robotics and Automation (ICRA), May 19-23, 2025, Atlanta, USA
[ICRA]Cooperative Flight Control Using Visual-Attention.movHuman-Robotic
Human-Robotic
Yin, L., Wang, T., Chahine, M., Seyde, T., Lechner, M., Hasani, R.M., & Rus, D. (2022). Towards Cooperative Flight Control Using Visual-Attention. 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 6334-6341.
Nonlinear control
Nonlinear control
Yin, L., Turesson, G., Tunestål, P., & Johansson, R. (2021). Sliding mode control on receding horizon: Practical control design and application. Control Engineering Practice, 109, 104724.
Predictive control
Predictive control
L. Yin, G. Turesson, P. Tunestål and R. Johansson, (2020). Model predictive control of an advanced multiple cylinder engine with partially premixed combustion concept. IEEE/ASME Transactions on Mechatronics, 25(2), 804-814.
Energy system
Energy system
Yin, L., Lundgren, M., Wang, Z., Stamatoglou, P., Richter, M., Andersson, Ö., & Tunestål, P. (2019). High efficient internal combustion engine using partially premixed combustion with multiple injections. Applied Energy, 233, 516-523.
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