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HTTP/2 301 server: GitHub.com content-type: text/html location: https://www.mengdixu.me/ x-github-request-id: CA69:15317B:74DA35:82E2EE:6950C7E7 accept-ranges: bytes age: 0 date: Sun, 28 Dec 2025 06:02:20 GMT via: 1.1 varnish x-served-by: cache-bom-vanm7210049-BOM x-cache: MISS x-cache-hits: 0 x-timer: S1766901741.614250,VS0,VE194 vary: Accept-Encoding x-fastly-request-id: 5e8b0fade75f5d32b23bcb7bec7f6c699efc0d29 content-length: 162 HTTP/2 200 server: GitHub.com content-type: text/html; charset=utf-8 last-modified: Sun, 14 Sep 2025 11:31:05 GMT access-control-allow-origin: * etag: W/"68c6a779-b27b" expires: Sun, 28 Dec 2025 06:12:21 GMT cache-control: max-age=600 content-encoding: gzip x-proxy-cache: MISS x-github-request-id: B4DF:2F7ECD:74A3EC:82AC0E:6950C7E6 accept-ranges: bytes age: 0 date: Sun, 28 Dec 2025 06:02:21 GMT via: 1.1 varnish x-served-by: cache-bom-vanm7210070-BOM x-cache: MISS x-cache-hits: 0 x-timer: S1766901741.919282,VS0,VE202 vary: Accept-Encoding x-fastly-request-id: fae6a5463d1472a85ad60cde0db1cfb52826bd33 content-length: 8326 Mengdi Xu

Mengdi Xu

I am an assistant professor at the Institute for Interdisciplinary Information Sciences (IIIS) at Tsinghua University. I was a postdoc at Stanford Vision and Learning Lab (SVL) working with Prof. Jiajun Wu and Prof. Fei-Fei Li. I received my PhD from Carnegie Mellon University with Best PhD Dissertation Award, advised by Prof. Ding Zhao (SafeAI Lab). I have spent wonderful summers at Google DeepMind Robotics, MIT-IBM Watson AI Lab, and Toyota Research Institute. I received a B.E. from Tsinghua University.

Email / CV (Mar 2024) / Google Scholar / Github / X

I am actively recruiting students to join my lab at Tsinghua IIIS and am also open to research collaborations. If you are interested, please feel free to reach out!

Prospective Students & Collaborations

Thank you for your interest in our lab! We welcome highly motivated undergraduates, graduate students, and visiting scholars to join us and collaborate. We are a highly interdisciplinary lab seeking students from diverse backgrounds, including robot hardware design, large-scale model training, machine learning, control, and robotics.
For undergraduates and graduate students, please complete this form and send me an email.
For potential collaborators, feel free to email me directly with the projects you are interested in.

Research

I am broadly interested in building scalable, adaptable, and reliable robots that seamlessly interact with humans in daily activities. Here are some research highlights:

In-context robot learning with few-shot demonstrations and non-expert data.
Unsupervised learning to discover task/skill structures in dynamics, visual geometries, and agent policies.
Distributionally robust RL that balances performance and robustness when facing task uncertainties.

Group

PhD students:

News

2025/09 - I joined Tsinghua IIIS as a tenure-track Assistant Professor.
2025/06 - ROSETTA won the best paper award at the RSS 2025 Workshop on Continual Robot Learning from Humans (CRLH).
2024/08 - One paper got accepted to JMLR.
2024/07 - Invited talk at CMU LeCAR Lab.
2024/06 - Joined Stanford Vision and Learning Lab (SVL) as a postdoctoral researcher.
2024/05 - We are organizing the RSS Workshop on Lifelong Robot Learning: Generalization, Adaptation, and Deployment with Large Models and RSS Pioneers 2024 Workshop. See you in Delft!
2023/10 - A new arXiv preprint RoboTool.
2023/08 - Two papers got accepted to CoRL 2023, and COVERS was accepted for an oral presentation.
2023/08 - Selected as EECS Rising Star.
2023/05 - Selected as RSS Pioneer.
2023/02 - Selected as Rising Star in Computational and Data Sciences.

Selected Publications

A full list of publications is here. (* indicates equal contribution.)

Few-shot Generalization

Creative Robot Tool Use with Large Language Models
Mengdi Xu*, Peide Huang*, Wenhao Yu*, Shiqi Liu, Xilun Zhang, Yaru Niu, Tingnan Zhang, Fei Xia, Jie Tan, Ding Zhao
CoRL 2023 Workshop on Language and Robot Learning: Language as Grounding
[paper] [webpage] [MLD Blog] [TechXplore]

Hyper-Decision Transformer for Efficient Online Policy Adaptation
Mengdi Xu, Yuchen Lu, Yikang Shen, Shun Zhang, Ding Zhao, Chuang Gan
The Eleventh International Conference on Learning Representations (ICLR), 2023
[paper] [webpage]

Prompting Decision Transformer for Few-shot Policy Generalization
Mengdi Xu, Yikang Shen, Shun Zhang, Yuchen Lu, Ding Zhao, Josh Tenenbaum, Chuang Gan
Thirty-ninth International Conference on Machine Learning (ICML), 2022
[paper] [webpage] [code]

Efficient Adaptation

Continual Vision-based Reinforcement Learning with Group Symmetries
Shiqi Liu*, Mengdi Xu*, Peide Huang, Yongkang Liu, Kentaro Oguchi, Ding Zhao
Conference on Robot Learning (CoRL), 2023 (oral)
[paper] [webpage] [code]

What Went Wrong? Closing the Sim-to-Real Gap via Differentiable Causal Discovery
Peide Huang, Xilun Zhang*, Ziang Cao*, Shiqi Liu*, Mengdi Xu, Wenhao Ding, Jonathan Francis, Bingqing Chen, Ding Zhao
Conference on Robot Learning (CoRL), 2023
[paper] [webpage]

Embodied Executable Policy Learning with Language-based Scene Summarization
Jielin Qiu*, Mengdi Xu*, William Han*, Seungwhan Moon, Ding Zhao
2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)
ICML 2023 Workshop on Interactive Learning with Implicit Human Feedback (spotlight)
[paper]

Functional Optimal Transport: Map Estimation and Domain Adaptation for Functional data
Jiacheng Zhu*, Aritra Guha*, Dat Do*, Mengdi Xu, XuanLong Nguyen, Ding Zhao
JMLR: Journal of Machine Learning Research
AAAI OT-SDM 2022 workshop (spotlight)
[paper] [code]

Robust and Safe Robot Learning

Group Distributionally Robust Reinforcement Learning with Hierarchical Latent Variables
Mengdi Xu, Peide Huang, Yaru Niu, Visak Kumar, Jielin Qiu, Chao Fang, Kuan-Hui Lee, Xuewei Qi, Henry Lam, Bo Li, Ding Zhao
The 26th International Conference on Artificial Intelligence and Statistics, (AISTATS), 2023
[paper] [webpage]

Scalable Safety-Critical Policy Evaluation with Accelerated Rare Event Sampling
Mengdi Xu, Peide Huang, Fengpei Li, Jiacheng Zhu, Xuewei Qi, Kentaro Oguchi, Zhiyuan Huang, Henry Lam, Ding Zhao
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2022
[paper]

Trustworthy Reinforcement Learning Against Intrinsic Vulnerabilities: Robustness, Safety, and Generalizability
Mengdi Xu*, Zuxin Liu*, Peide Huang*, Wenhao Ding, Zhepeng Cen, Bo Li, Ding Zhao
Preprint, under review
[paper]

Talks

Research Overview March 2024

Awards

Best PhD Dissertation Award 2024, Mechanical Engineering, CMU, 2024
EECS Rising Stars, 2023
Robotics: Science and Systems (RSS) Pioneers, 2023
Rising Stars in Computational and Data Sciences, 2023

Service

Co-organized RSS 2024 Workshop on Lifelong Robot Learning: Generalization, Adaptation, and Deployment with Large Models
Co-organized RSS Pioneers Workshop 2024
Breakout session leader at 3rd Women in Machine Learning Un-Workshop, ICML 2022
Mentor of 2020 CMU Robotics Institute Summer Scholars Program (RISS)
Conference Reviewer: NeurIPS, ICML, ICLR, CoRL, AISTATS, ACL, ICRA, ICCV, ECCV, CVPR, AAAI, L4DC, ICASSP
Journal Reviewer: IJRR, RA-L, T-ITS, T-IV

Mentoring

Xilun Zhang (Master, CMU → PhD, Stanford)
Shiqi Liu (Master, CMU → PhD, CMU)
Ziang Cao (Undergrad, UPitt → Master, Stanford)

Website template from Jon Barron.

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