Papers
1
Total Citations
11
H-Index
1
About
Yishan Feng is a robotics researcher whose work centers on intelligent locomotion and autonomous navigation for legged robots in unstructured environments. Her key contributions lie at the intersection of reinforcement learning and robotic control, particularly for hexapod platforms operating under sensor constraints. In her most-cited work, "A Soft Actor-Critic Approach for a Blind Walking Hexapod Robot with Obstacle Avoidance" (2023, 11 citations), Feng tackles the challenge of path planning when external sensors are compromised by weather or lighting. By proposing a model-free reinforcement learning framework, she enables a hexapod to navigate and avoid obstacles using only proprioceptive feedback—effectively "blind" locomotion. This approach is significant for field robotics, where sensor reliability is often unpredictable. Her work demonstrates how soft actor-critic algorithms can be adapted for real-world robotic systems, bridging the gap between simulation and deployment. Feng’s research is particularly relevant for applications in disaster response, agriculture, and planetary exploration, where robust, sensor-limited mobility is critical. Her contributions highlight the growing role of learning-based methods in making legged robots more resilient and autonomous in challenging terrains.
Research Focus
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Top Papers
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