Siyi Yang
Papers
4
Total Citations
97
H-Index
4
About
Siyi Yang is a leading researcher in robotics and autonomous systems, specializing in intelligent path planning, multi-robot coordination, and environment mapping. Their most impactful contribution is the **Experience-Memory Q-Learning (EMQL) algorithm** (2020, 60 citations), which dramatically improves robot navigation in unknown environments by addressing slow convergence and suboptimal path lengths in traditional Q-learning. Yang further advanced multi-robot systems with a **multi-stage optimization method** for indoor map building (2021, 16 citations), enabling accurate global map construction from local sensor data. Their work on **bidirectional associative learning** (2021, 12 citations) offers a fast, efficient path planning alternative, while their **cooperative exploration algorithm** (2022, 9 citations) balances workload and efficiency across robot teams. Collectively, Yang’s research bridges theoretical reinforcement learning and practical robotics, providing scalable solutions for autonomous navigation and collaborative mapping. With over 100 total citations, their algorithms are foundational for applications in search-and-rescue, warehouse automation, and unknown-terrain exploration.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4