Papers
12
Total Citations
189
H-Index
7
About
Sha Luo is a robotics researcher whose work spans reinforcement learning, motion planning, and robot perception, with a particular focus on making autonomous robots more efficient, intelligent, and adaptable in complex real-world environments. Among her most notable contributions is her work on curriculum learning for robotic reaching tasks, which accelerated reinforcement learning training by structuring the learning process progressively — a paper that has garnered 52 citations and helped address one of the field's central bottlenecks: the scarcity of high-quality training data. Her research on self-imitation learning and experience-based planning further advances this agenda, developing methods that allow robots to bootstrap their own improvement from past successes. Luo has also made significant contributions to path planning, including surveys of RRT-based approaches and novel improvements to bidirectional RRT algorithms for robotic manipulators, collectively accumulating dozens of citations. Her work on simultaneous object recognition and grasping reflects a broader vision of robots that can perceive and act fluidly in open-ended settings. Early work on deep learning-based robot detection for RoboCup demonstrates her strong foundation in applied computer vision, making her a well-rounded and impactful voice in modern robotics research.
Research Focus
Key Achievements
Top Papers
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- 4Robot detection and localization based on deep learning20 citations · 2017
- 5Self-Imitation Learning by Planning17 citations · 2021
- 6Path Planning of the Robotic Manipulator Based on an Improved Bi-RRT14 citations · 2024
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