Robotics Seminar: Danfei Xu

On April 10, 2025, Danfei Xu presented “Generative Task and Motion Planning” as part of the Michigan Robotics Seminar Series. Abstract Long-horizon planning is fundamental to our ability to solve complex physical problems, from using tools to cooking dinners. Despite recent progress in commonsense-rich foundation models, the ability to do the same is still lacking in robots, particularly with learning-based approaches. In this talk, I will present a body of work that aims to transform Task and Motion Planning—one of the most powerful computational frameworks in robot planning—into a fully generative model framework, enabling compositional generalization in a largely data-driven approach. I will explore how to chain together modular diffusion-based skills through iterative forward-backward denoising, how to formulate TAMP as a factor graph problem with generative models serving as learned constraints for planning, and how to integrate task and motion planning within a single generative process. I'll conclude by discussing the “reasoning” paradigms of recent robot foundation models and the need to move beyond language-aided chain of thought, and the prospect of extracting planning representations from demonstrations. Bio Danfei Xu is an Assistant Professor in the School of Interactive Computing at Georgia Institute of Technology, where he directs the Robot Learning and Reasoning Lab (RL2). He is also a researcher at NVIDIA AI. He earned his Ph.D. in Computer Science from Stanford University in 2021. His research focuses on machine learning methods for robotics, particularly in manipulation planning and imitation learning. His work has received Best Paper nominations at the Conference on Robot Learning (CoRL) and IEEE Robotics and Automation Letters (RA-L). His research is funded by National Science Foundation, Autodesk Research, and Meta Platforms. More: Michigan Robotics Seminar Series: https://robotics.umich.edu/events/robotics-seminar-series/ Danfei Xu: https://faculty.cc.gatech.edu/~danfei/