Ruiqi Zhang
Papers
1
Total Citations
5
H-Index
1
About
Ruiqi Zhang is a rising researcher in multi-robot systems and reinforcement learning, with a focus on scalable, safe navigation for large-scale multi-agent systems (MAS). Their most-cited work, "PIPO: Policy Optimization with Permutation-Invariant Constraint for Distributed Multi-Robot Navigation" (2022, 5 citations), addresses a critical bottleneck in the field: the trade-off between scalability and safety. While centralized methods falter as agent numbers grow, and decentralized approaches often lack coordination, Zhang introduces a permutation-invariant constraint that enables distributed policies to maintain safety without sacrificing efficiency. This innovation allows robots to navigate complex, crowded environments while respecting the symmetry inherent in multi-agent teams. By bridging the gap between theoretical guarantees and practical deployment, Zhang’s work has implications for swarm robotics, warehouse automation, and autonomous driving. Though early in their career, Zhang’s contributions signal a deep commitment to solving real-world coordination challenges, making them a promising voice in the next generation of robotics and AI researchers.
Research Focus
Key Achievements
Top Papers
- 1