Peide Huang

I am a final-year Ph.D. student advised by Prof. Ding Zhao@SafeAI Lab at Carnegie Mellon University. I am also fortunate to work with Prof. Fei Fang@AI and Social Good Lab at CMU. Prior to joining CMU, I received my Bachelor's degree from Nanyang Technological University, Singapore and Master's degree from Stanford University.

My research goal is to understand the interplay between the learning agent and the tasks, with the objective to enable safe, robust, and efficient decision making. To achieve this goal, I leverage curriculum learning, game theory, and foundation models. I also tackle real-world applications in robotics and autonomous driving.

I am open to collaboration. If you are interested, please contact me via email.

Email  /  CV  /  Google Scholar  /  Twitter


  • 11/2023: Our RoboTool is covered by TechXplore as a featured story!
  • 08/2023: Two papers (one Oral) are accepted by CoRL 2023!
  • 04/2023: I was awarded a fellowship from the CMU Machine Learning Department.
  • 01/2023: Group Distributionally Robust RL is accepted by AISTATS 2023!
  • 10/2022: I received Top Reviewer Award and Scholar Award from NeurIPS 2022 Committee.
  • 09/2022: GRADIENT is accepted by NeurIPS 2022! Thanks to all the co-authors!
  • 06/2022: Our work on accelerated policy evaluation in the presence of rare events is accepted by IROS 2022.
  • 04/2022: My first conference paper is accepted by IJCAI 2022. Thank all the co-authors!


* indicates equal contribution

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

Preprint, abridged in Foundation Models for Decision Making Workshop@NeurIPS2023

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

7th Annual Conference on Robot Learning (CoRL 2023)

Continual Vision-based Reinforcement Learning with Group Symmetries

Shiqi Liu*, Mengdi Xu*, Peide Huang, Xilun Zhang, Yongkang Liu, Kentaro Oguchi, Ding Zhao

7th Annual Conference on Robot Learning (CoRL 2023) (Oral, 6.6%)

Curriculum Reinforcement Learning using Optimal Transport via Gradual Domain Adaptation

Peide Huang, Mengdi Xu, Jiacheng Zhu, Laixi Shi, Fei Fang, Ding Zhao

The 36th Conference on Neural Information Processing Systems (NeurIPS 2022)

Robust Reinforcement Learning as a Stackelberg Game via Adaptively-Regularized Adversarial Training

Peide Huang, Mengdi Xu, Fei Fang, Ding Zhao

The 31st International Joint Conference on Artificial Intelligence (IJCAI 2022)

CaDRE: Controllable and Diverse Generation of Safety-Critical Driving Scenarios using Real-World Trajectories

Peide Huang, Wenhao Ding, Jonathan Francis, Bingqing Chen, Ding Zhao


Gradient Shaping for Multi-Constraint Safe Reinforcement Learning

Yihang Yao, Zuxin Liu, Zhepeng Cen, Peide Huang, Tingnan Zhang, Wenhao Yu, Ding Zhao

6th Annual Learning for Dynamics & Control Conference (L4DC 2024)

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)

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

2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2022)

Abridged in ICLR 2021 Workshop in Security and Safety in Machine Learning Systems

Multimodal Representation Learning of Cardiovascular Magnetic Resonance Imaging

Jielin Qiu*, Peide Huang*, Makiya Nakashima, Jaehyun Lee, Jiacheng Zhu, Wilson Tang, Pohao Chen, Christopher Nguyen, Byung-Hak Kim, Debbie Kwon, Douglas Weber, Ding Zhao, David Chen

ICML 2023 Workshop on Machine Learning for Multimodal Healthcare Data (ML4MHD 2023)

Latent Goal Allocation for Multi-Agent Goal-Conditioned Self-Supervised Imitation Learning

Peide Huang*, Laixi Shi*, Rui Chen*

NeurIPS 2021 Workshop in Bayesian Deep Learning

Curriculum Vitae

Click here to download my full CV (last update: Feb 2024).


Carnegie Mellon University

Aug 2020 -- Present


Specialization: reinforcement learning, deep learning, and robotics

Carnegie Mellon University

Aug 2023 -- Present


Specialization: machine learning

Stanford University

Aug 2018 -- June 2020


Specialization: robotics, control

Nanyang Technological University, Singapore

Aug 2014 -- June 2018


Specialization: aeronautics and space engineering


Apple, AIML

May 2024 -- Aug 2024

Machine Learning Research Intern

Responsibility: work on robotic foundation models.

Bosch Center for Artificial Intelligence

May 2023 -- Aug 2023

Machine Learning Research Intern

Responsibility: developed a safety-critical scenenario generation algorithm for self-driving vehicles testing.

Flexiv Robotics Ltd.

Aug 2018 -- June 2020

System Engineering Intern

Responsibility: developed a robot evaluation platform in Simulink and a communication protocal in C++ for fast prototyping.

Agency for Science, Technology and Research, Singapore

Jan 2017 -- June 2017

Research Assistant

Responsibility: designed a mobile robot that can change its morphology according to the environment constraints.

Academic Services


  • Journal: TPAMI, IJCV