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About

Changyun Wei is a robotics and artificial intelligence researcher whose work spans multi-robot systems, task allocation, reinforcement learning, and autonomous navigation. With a career building from foundational work in cognitive robot architectures and cooperative coordination protocols, Wei has made significant contributions to how robot teams communicate, plan, and collaborate in complex environments. Wei's most influential contribution — garnering 159 citations — introduced a Multi-Objective Particle Swarm Optimization framework for cooperative multi-robot task allocation, elegantly balancing team efficiency with workload fairness. This work exemplifies his broader expertise in optimization-driven approaches to multi-agent systems. Complementing this, his 2021 paper on Hierarchical Reinforcement Learning for multistep robotic manipulation (88 citations) demonstrates a forward-looking integration of symbolic planning with low-level motion control, addressing one of robotics' most persistent challenges. Wei's earlier research established important groundwork in decentralized pathfinding, communication-driven coordination, and search-and-retrieval tasks, while more recent work explores deep reinforcement learning for decentralized multi-robot path planning and adaptive sensor fusion for indoor positioning. Collectively accumulating nearly 500 citations, Wei's research portfolio reflects a consistent commitment to making autonomous robot teams more intelligent, adaptable, and practically deployable across real-world scenarios.