Wenhao Ding (丁文浩)
Email: wenhaod AT nvidia DOT com
I am a Research Scientist in the Autonomous Vehicle Group at NVIDIA Research. I received my Ph.D. from Carnegie Mellon University and my Bachelor's degree from Tsinghua University. I am interested in improving the safety of autonomous systems using simulation, reinforcement learning, and causal discovery.
News & Updates
2024/07 - We are organizing the 2024 IEEE International Automated Vehicle Validation Conference. Consider submitting your paper and special session proposals!
2024/06 - I join NVIDIA Research as a Research Scientist at the Autonomous Vehicle Group.
2024/03 - One paper using causal-aware representation for driving is accepted to RA-L.
2024/02 - One paper about privacy in robotics is accepted to ICRA 2024.
2023/09 - One paper combining causality and robustness is accepted to NeurIPS 2023.
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!
2022/08 - My research proposal wins 2022 Qualcomm Innovation Fellowship, North America.
Selected Publications
RealGen: Retrieval Augmented Generation for Controllable Traffic Scenarios
Wenhao Ding*,
Yulong Cao*,
Ding Zhao,
Chaowei Xiao,
Marco Pavone
European Conference on Computer Vision (ECCV) 2024
Safety-aware Causal Representation for Trustworthy Reinforcement Learning in Autonomous Driving
Haohong Lin,
Wenhao Ding,
Zuxin Liu,
Yaru Niu,
Jiacheng Zhu,
Yuming Niu,
Ding Zhao
IEEE Robotics and Automation Letters (RA-L) 2024
arXiv / code / bibtex
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
Your Room is not Private: Gradient Inversion Attack for Deep Q-Learning
Miao Li,
Wenhao Ding,
Ding Zhao
International Conference on Robotics and Automation (ICRA) 2024, Japan
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
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
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
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
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
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
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
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
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
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
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
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
Academic Services
Conference Reviewer: ICML, ICLR, NeurIPS, AISTATS, CVPR, ECCV, ICCV, CoRL, ICRA, IROS, IJCAI, ICASSP, ITSC, ICME
Journal Reviewer: TMLR, IEEE RA-L, IEEE TNNLS, IEEE T-ITS, IEEE T-IV, IEEE Access, IEEE TII, IEEE MM, Scientific Report
Organizer: CVPR 2023 Secure and Safe Autonomous Driving Workshop and Challenge, ICRA 2022 SeasonDepth Challenge