About

Peide Huang

Email: peide_huang at apple dot com

I am a researcher at Apple AIML . I work on machine learning and robotics. I obtained my B.E. from Nanyang Technological University, M.S. from Stanford, and Ph.D. from Carnegie Mellon University.

Email  /  CV  /  Google Scholar  /  Twitter


News

  • 09/2024: I joined Apple AIML!
  • 09/2024: I defended my thesis titled "Co-evolving Environments and Agents for Physical-World Deployments"!
  • 11/2023: Our RoboTool is covered by TechXplore as a featured story!
  • 08/2023: Two papers (one Oral) are accepted by CoRL 2023!

Publications

* indicates equal contribution

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

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

2025 IEEE International Conference on Robotics and Automation (ICRA 2025)

Paper


Dynamics as Prompts: In-Context Learning for Sim-to-Real System Identifications

Xilun Zhang*, Shiqi Liu*, Peide Huang, William Jongwon Han, Yiqi Lyu, Mengdi Xu*, Ding Zhao

IEEE Robotics and Automation Letters (RA-L)

Paper Website


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)

Paper BibTeX


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

Paper Website BibTeX Blog Media 1 Media 2


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)

Paper Website BibTeX


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 BibTeX


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%)

Paper Website BibTeX


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)

Paper BibTeX


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)

Paper BibTeX


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)

Paper BibTeX


Curriculum Vitae

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

Education

Carnegie Mellon University

Aug 2020 -- Sep 2024

Ph.D. Reinforcement Learning and Robotics

M.S. Machine Learning

Stanford University

Aug 2018 -- June 2020

M.S. Robotics and Control

Nanyang Technological University, Singapore

Aug 2014 -- June 2018

B.E. Aeronautics and Space Engineering

Work

Apple

May 2024 -- Now

Machine Learning Researcher

Bosch Center for Artificial Intelligence

May 2023 -- Aug 2023

Machine Learning Research Intern

Flexiv Robotics

Aug 2018 -- June 2020

System Engineering Intern

Agency for Science, Technology and Research, Singapore

Jan 2017 -- June 2017

Research Assistant

Academic Services

Reviewer

  • Conference: NeurIPS, ICML, ICLR, CoRL, RLC, AISTATS, ICASSP, CVPR
  • Journal: TPAMI, IJCV

Teaching