Khoa "Albert" Vo
Computer science
Hometown: Da Nang, Hai Chau, Viet Nam
Graduation date: Spring 2026
FURI | Spring 2025
Bridging the Sim-to-Real Gap in Autonomous Systems Using Learning from Demonstration
This research examines the effectiveness of Learning from Demonstration (LfD) techniques in transferring knowledge from simulated to real-world environments for autonomous driving tasks. The study utilizes the Duckietown platform to compare naive imitation learning methods, including behavior cloning, against LfD approaches through metrics including accumulated reward, average distance traveled, and survival time. Results indicate that LfD achieves comparable performance while requiring fewer real-world interactions. This work provides insight into how to leverage task-relevant external data, facilitating effective knowledge transfer from simulation to real-world environments. Future research will explore combining LfD with other transfer techniques or conducting experiments with diverse driving tasks to better understand the effectiveness of this learning approach.
Mentor: Hua Wei
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