Ding Zhao

About

Ding Zhao is a robotics and artificial intelligence researcher whose work spans multi-agent systems, safe reinforcement learning, and sim-to-real transfer for autonomous robots. His most influential contribution, MAPPER, introduced a decentralized evolutionary reinforcement learning framework for multi-agent path planning in dynamic environments—a practically critical challenge for large-scale robot fleet deployment—garnering over 100 citations and establishing him as a notable voice in autonomous navigation research. Zhao's work consistently bridges theoretical rigor and real-world applicability: his research on robust reinforcement learning formulates adversarial training as a Stackelberg game to improve agent resilience under model errors, while his constrained variational policy optimization framework addresses safety guarantees in RL deployment, a growing concern as autonomous systems enter high-stakes environments. He has also advanced active perception through decision transformer-based object detection and tackled the persistent sim-to-real gap using in-context learning for system identification. His benchmark suite for offline safe RL reflects a broader commitment to community infrastructure and reproducible research. Together, Zhao's contributions reveal a coherent research vision: making autonomous agents not only capable and adaptive, but reliably safe and deployable in the messy complexity of the real world.

Research Focus

Key Achievements

Top Papers

1. 1
   MAPPER: Multi-Agent Path Planning with Evolutionary Reinforcement Learning in Mixed Dynamic Environments

   113 citations · 2020
2. 2
   Recurrent Attentive Neural Process for Sequential Data

   23 citations · 2019
3. 3
   Robust Reinforcement Learning as a Stackelberg Game via Adaptively-Regularized Adversarial Training

   22 citations · 2022
4. 4
   Learning to View: Decision Transformers for Active Object Detection

   16 citations · 2023
5. 5
   Constrained Variational Policy Optimization for Safe Reinforcement Learning

   16 citations · 2022
6. 6
   MAPPER: Multi-Agent Path Planning with Evolutionary Reinforcement Learning in Mixed Dynamic Environments

   12 citations · 2020
7. 7
   Robust unsupervised learning of temporal dynamic vehicle-to-vehicle interactions

   7 citations · 2022
8. 8
   GOATS: Goal Sampling Adaptation for Scooping with Curriculum Reinforcement Learning

   7 citations · 2023
9. 9
   Dynamics as Prompts: In-Context Learning for Sim-to-Real System Identifications

   6 citations · 2025
10. 10
    Datasets and Benchmarks for Offline Safe Reinforcement Learning

    5 citations · 2023

Key Collaborators

Contact & Links