Guntitat Sawadwuthikul
Papers
1
Total Citations
34
H-Index
1
About
Guntitat Sawadwuthikul is a researcher advancing human-robot interaction, with a focus on making robotic systems more adaptive in dynamic, real-world environments. His primary research areas include visual goal communication, few-shot learning, and service robotics. His most cited work, "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 conventional waiter robots: their reliance on static, predefined goal positions. Sawadwuthikul proposed a novel framework that enables robots to interpret and respond to dynamic human cues, such as pointing or gesturing toward a new location, using few-shot learning to generalize from minimal examples. This contribution is especially impactful for settings like coffee shops or outdoor catering events, where customer positions and requests frequently change. By shifting from rigid position control to flexible, vision-based communication, his work enhances the practicality and autonomy of service robots. Sawadwuthikul’s research demonstrates a clear commitment to bridging the gap between laboratory robotics and real-world deployment, offering scalable solutions that improve robot responsiveness in human-centered spaces.
Research Focus
Key Achievements
Top Papers
- 1