Quantao Yang

I'm a postdoc researcher at KTH Royal Institute of Technology, working with Prof. Olov Andersson and Prof. Danica Kragic. I'm passionate about developing intelligent robotic agents capable of executing tasks across various real-world environments.

I did my PhD in Computer Science at Örebro University, where I was advised by Prof. Todor Stoyanov and Prof. Johannes A. Stork. I was a visiting PhD student at UT Austin advised by Prof. Yuke Zhu.

Email / Scholar / Twitter / Github

Research

I'm interested in robot learning.

	S²-Diffusion: Generalizing from Instance-level to Category-level Skills in Robot Manipulation Quantao Yang, Michael C. Welle, Danica Kragic, Olov Andersson IEEE Robotics and Automation Letters (RA-L), 2025 Project Paper
	Diffusion Trajectory-guided Policy for Long-horizon Robot Manipulation Shichao Fan, Quantao Yang, Yajie Liu, Kun Wu, Zhengping Che, Qingjie Liu, Min Wan IEEE Robotics and Automation Letters (RA-L), 2025 Project Paper
	One Map to Find Them All: Real-time Open-Vocabulary Mapping for Zero-shot Multi-Object Navigation Finn Busch, Timon Homberger, Jesús Ortega-Peimbert, Quantao Yang, Olov Andersson IEEE International Conference on Robotics and Automation (ICRA), 2025 Project Paper Code
	PRIME: Scaffolding Manipulation Tasks with Behavior Primitives for Data-efficient Imitation Learning Tian Gao, Soroush Nasiriany, Huihan Liu, Quantao Yang, Yuke Zhu IEEE Robotics and Automation Letters (RA-L), 2024 Project Paper Code
	Learn from Robot: Transferring Skills for Diverse Manipulation via Cycle Generative Networks Quantao Yang, Johannes Andreas Stork, Todor Stoyanov IEEE International Conference on Automation Science and Engineering (CASE), 2023 Paper
	Variable Impedance Skill Learning for Contact-rich Manipulation Quantao Yang, Alexander Dürr, Elin Anna Topp, Johannes Andreas Stork, Todor Stoyanov IEEE Robotics and Automation Letters (RA-L), 2022 Paper
	MPR-RL: Multi-Prior Regularized Reinforcement Learning for Knowledge Transfer Quantao Yang, Johannes Andreas Stork, Todor Stoyanov IEEE Robotics and Automation Letters (RA-L), 2022 Paper
	Null Space Based Efficient Reinforcement Learning with Hierarchical Safety Constraints Quantao Yang, Johannes Andreas Stork, Todor Stoyanov IEEE European Conference on Mobile Robots (ECMR), 2021 Paper

Service

Reviewer for RAL, TMech, ICRA, IROS, CoRL

Design and source code from Jon Barron's website.