Thanawat Lodkaew
Papers
1
Total Citations
34
H-Index
1
About
Thanawat Lodkaew is a robotics researcher whose work lies at the intersection of human-robot interaction, few-shot learning, and autonomous navigation. His most-cited paper, "Visual Goal Human-Robot Communication Framework With Few-Shot Learning: A Case Study in Robot Waiter System" (2021, 34 citations), addresses a critical limitation in service robotics: the inability of waiter robots to adapt to dynamic, unstructured environments like coffee shops or outdoor events. Rather than relying on static, pre-mapped goal positions, Lodkaew’s framework enables robots to interpret visual cues from human gestures or objects using few-shot learning, allowing them to navigate to new, user-specified locations with minimal training data. This contribution is significant because it bridges the gap between rigid industrial automation and the flexibility required for real-world service applications. Lodkaew’s work demonstrates how combining computer vision with lightweight machine learning can make robots more intuitive and responsive to human needs. By focusing on practical deployment challenges, his research offers a scalable path toward more autonomous and socially aware service robots, earning recognition among researchers working on adaptive human-robot communication systems.
Research Focus
Key Achievements
Top Papers
- 1