Xing Li
Photo: SCIoI
Xing Li is a roboticist with a background in machine learning and learning from demonstration. His research revolves around how robots can acquire complex, contact-rich manipulation skills from very limited human demonstrations and generalize them to new objects and situations.
He joined SCIoI as a doctoral researcher in August 2020 under the supervision of Prof. Oliver Brock.
At SCIoI, he studied how robots can learn complex, contact-rich manipulation tasks from very limited human demonstrations. His work focused on helping robots generalize what they have learned to new objects and situations. His doctoral thesis (see below), examines how environmental constraints can help robots build general manipulation policies from a single demonstration.
Projects
Xing Li is member of:
Doctoral Thesis
Publications +
6984777
Xing Li
1
apa
50
date
year
19933
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Pfisterer, A., Li, X., Mengers, V., & Brock, O. (2025). A Helping (Human) Hand in Kinematic Structure Estimation. 2025 IEEE International Conference on Robotics and Automation (ICRA), 11918–11925. https://doi.org/10.1109/ICRA55743.2025.11127847
Mengers, V., Koenig, A., Li, X., Sieler, A., Battaje, A., & Brock, O. (2025). Stop Merging, Start Separating: Why Merging Learning and Modeling Won’t Solve Manipulation but Separating the General From the Specific Will. International Conference on Robotics & Automation (ICRA) Workshop: Learning Meets Model-Based Methods for Contact-Rich Manipulation.
Li, X., Zenkri, O., Pfisterer, A., & Brock, O. (2024). A Biologically Inspired Design Principle for Building Robust Robotic Systems. arXiv. https://doi.org/10.48550/ARXIV.2408.10192
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