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Wenhao Ding (丁文浩)

Email: wenhaod AT andrew DOT cmu DOT edu

I am a final-year Ph.D. in Safe AI Lab at Carnegie Mellon University, co-advised by Prof. Ding Zhao and Prof. Bo Li. I received my Bachelor degree from Electronic Engineering at Tsinghua University and my Master's degree from Machine Learning Department at CMU.

I am an FPV drone flyer. Check my gallery.

Research Highlights

My research goal is to build robust, safe, and interpretable robots that can semantically understand the physical world. To achieve this goal, I work on the following directions:

News & Updates

2023/09 - One paper combining causality and robustness is accepted to NeurIPS 2023.

2023/06 - I will start my internship in NVIDIA Research Autonomous Vehicle Research Group.

2023/04 - One paper about reward-conditioned offline RL accepted to ICML 2023.

2022/12 - My research is covered by CMU Engineering News Driving autonomy into the metaverse!

2022/10 - Glad to present our work about causal discovery in Wayve!

2022/09 - Two papers got accepted to NeurIPS 2022. See you in New Orleans!

2022/08 - My research proposal "Safety-critical Scenarios Generation and Generalization for Autonomous Driving" wins 2022 Qualcomm Innovation Fellowship, North America.

Selected Publications

Seeing is not Believing: Robust Reinforcement Learning against Spurious Correlation

*Wenhao Ding, *Laixi Shi, Yuejie Chi, Ding Zhao
Conference on Neural Information Processing Systems (NeurIPS) 2023, New Orleans

arXiv / code / bibtex

Semantically Adversarial Driving Scenario Generation with Explicit Knowledge Integration

Wenhao Ding, Haohong Lin, Bo Li, Kim Ji Eun, Ding Zhao
Workshop on Environment Generation for Generalizable Robots (EGG) at RSS 2023
Workshop on Knowledge and Logical Reasoning in the Era of Data-driven Learning at ICML 2023

arXiv / code / bibtex

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, Atlanta

arXiv / code / bibtex

Bayesian Reparameterization of Reward-Conditioned Reinforcement Learning with Energy-based Models

*Wenhao Ding, *Tong Che, Ding Zhao, Marco Pavone
International Conference on Machine Learning (ICML) 2023, Hawaii

arXiv / code / bibtex

Learning to View: Decision Transformers for Active Object Detection

Wenhao Ding, Nathalie Majcherczyk, Mohit Deshpande, Xuewei Qi, Ding Zhao, Rajasimman Madhivanan, Arnie Sen
IEEE International Conference on Robotics and Automation (ICRA) 2023, London

arXiv / code / bibtex

Generalizing Goal-Conditioned Reinforcement Learning with Variational Causal Reasoning

Wenhao Ding, Haohong Lin, Bo Li, Ding Zhao
Conference on Neural Information Processing Systems (NeurIPS) 2022, New Orleans

arXiv / code / bibtex

SafeBench: A Benchmarking Platform for Safety Evaluation of Autonomous Vehicles

*Chejian Xu, *Wenhao Ding, Weijie Lyu, Zuxin Liu, Shuai Wang, Yihan He, Hanjiang Hu, Ding Zhao, Bo Li
Conference on Neural Information Processing Systems (NeurIPS) 2022, New Orleans

website / arXiv / code / bibtex

A Survey on Safety-Critical Driving Scenario Generation -- A Methodological Perspective

Wenhao Ding, Chejian Xu, Mansur Arief, Haohong Lin, Bo Li, Ding Zhao
IEEE Transactions on Intelligent Transportation Systems (T-ITS), March, 2023

arXiv / Xplore / code / bibtex

CausalAF: Causal Autoregressive Flow for Safety-Critical Driving Scenario Generation

Wenhao Ding, Haohong Lin, Bo Li, Ding Zhao
Conference on Robot Learning (CoRL) 2022, New Zealand
Abridged in ICML 2022 Workshop on Safe Learning for Autonomous Driving

arXiv / code / bibtex

Multimodal Safety-Critical Scenarios Generation for Decision-Making Algorithms Evaluation

Wenhao Ding, Baiming Chen, Bo Li, Kim Ji Eun, Ding Zhao
IEEE Robotics and Automation Letters (RA-L) with ICRA 2021, Xi'an

arXiv / code / bibtex

Task-Agnostic Online Reinforcement Learning with an Infinite Mixture of Gaussian Processes

Mengdi Xu, Wenhao Ding, Jiacheng Zhu, Zuxin Liu, Baiming Chen, Ding Zhao
Neural Information Processing Systems (NeurIPS) 2020, Vancouver

arXiv / code / bibtex

Learning to Collide: An Adaptive Safety-Critical Scenarios Generating Method

Wenhao Ding, Baiming Chen, Minjun Xu, Ding Zhao
International Conference on Intelligent Robots and Systems (IROS) 2020, Las Vegas

arXiv / code / video / bibtex

CMTS: Conditional Multiple Trajectory Synthesizer for Generating Safety-critical Driving Scenarios

Wenhao Ding, Mengdi Xu, Ding Zhao
International Conference on Robotics and Automation (ICRA) 2020, Paris

arXiv / code / supplementary / bibtex

Preprints

Your Room is not Private: Gradient Inversion Attack for Deep Q-Learning

Miao Li, Wenhao Ding, Ding Zhao
Preprint arXiv:2306.09273

arXiv / code / bibtex

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 arXiv:2209.08025

arXiv / code / bibtex

Academic Services

Conference Reviewer: ICML, ICLR, NeurIPS, AISTATS, CVPR, ECCV, ICCV, ICRA, IROS, ICASSP, ITSC, ICME

Journal Reviewer: TMLR, IEEE RA-L, IEEE TNNLS, IEEE T-ITS, IEEE T-IV, IEEE Access, IEEE TII, IEEE MM

Organizer: CVPR 2023 Secure and Safe Autonomous Driving Workshop and Challenge