Yihai Duan
Papers
2
Total Citations
35
H-Index
2
About
Yihai Duan is a researcher focused on developmental robotics and autonomous navigation, with key contributions in emergent reinforcement learning and mobile robot exploration. His most cited work, "Motivated Optimal Developmental Learning for Sequential Tasks Without Using Rigid Time-Discounts" (2018, 29 citations), introduces a novel approach to reinforcement learning that replaces traditional symbolic representations—like Q-learning’s handcrafted states—with emergent representations for hidden neurons. This allows robots to learn sequential tasks without rigid time-discounts, mimicking natural developmental processes. Duan’s research demonstrates how motivated optimal learning can enable more adaptive and human-like decision-making in artificial systems. In his earlier work, "Environment exploration and map building of mobile robot in unknown environment" (2015, 6 citations), he proposed a real-time strategy using laser sensor data for simultaneous exploration and obstacle avoidance, addressing challenges in producing accurate maps of unknown spaces. While his citation counts are modest, Duan’s focus on emergent, biologically inspired learning and practical navigation solutions marks him as a thoughtful contributor to developmental robotics and autonomous systems, with potential for growing influence in the field.
Research Focus
Key Achievements
Top Papers
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