Chuanneng Sun

About

Chuanneng Sun is a rising researcher at the forefront of multi-robot systems and reinforcement learning, with a focus on enabling intelligent, autonomous decision-making in complex, high-stakes environments. His work centers on developing novel RL frameworks for task allocation, pathfinding, and coordinated search under uncertainty, particularly for post-disaster rescue and informed search missions. Sun’s major contributions include a multi-behavior multi-agent RL approach that leverages offline training to improve exploration efficiency, and a bi-layer joint training framework designed to optimize rescue operations. His most cited paper (10 citations, 2024) introduces a pioneering method for informed search, while subsequent works extend these ideas to address environment uncertainty and integrate large language models for task planning. Sun’s research demonstrates strong practical impact, with cumulative citations reflecting growing interest in his scalable, robust solutions for real-world robotics challenges. His recent work on retrieval-augmented hierarchical in-context RL marks a significant step toward bridging LLMs and reinforcement learning, promising more adaptive and context-aware autonomous systems.

Research Focus

Key Achievements

Top Papers

1. 1
   Multi-Behavior Multi-Agent Reinforcement Learning for Informed Search via Offline Training

   10 citations · 2024
2. 2
   Reinforcement learning–based task allocation and path‐finding in multi‐robot systems under environment uncertainty

   4 citations · 2025
3. 3
   A Bi-Layer Joint Training Reinforcement Learning Framework for Post-Disaster Rescue

   4 citations · 2024
4. 4
   Retrieval-Augmented Hierarchical in-Context Reinforcement Learning and Hindsight Modular Reflections for Task Planning with LLMs

   2 citations · 2025

Key Collaborators

Contact & Links