Teerawat Piriyatharawet
Papers
2
Total Citations
7
H-Index
2
About
Teerawat Piriyatharawet is a rising researcher in the field of robotics, with a focused interest in intelligent manipulation and safe human-robot collaboration. His work addresses two critical challenges in modern automation: enhancing robotic dexterity and ensuring operational safety in shared workspaces. His most cited paper, "Deep Learning LSTM-Based Slip Detection for Robotic Grasping" (2023, 5 citations), introduces a novel deep learning approach using Long Short-Term Memory networks to detect slip in real-time, a key advancement for reliable grasping in mixed-volume logistics and manufacturing. This work directly supports the growing demand for adaptable robots in "Any-Mixed-Any-Volume" scenarios. Complementing this, his paper "Towards Safe and Efficient Human-Robot Collaboration: Motion Planning Design in Handling Dynamic Obstacles" (2023, 2 citations) proposes an extended motion planning framework that enables robots to dynamically re-route and re-plan trajectories when encountering unpredictable human movements. Together, these contributions demonstrate Piriyatharawet’s commitment to bridging the gap between robust robotic performance and human-centric safety, marking him as a promising voice in the next generation of collaborative robotics research.
Research Focus
Key Achievements
Top Papers
- 1Deep Learning LSTM-Based Slip Detection for Robotic Grasping5 citations · 2023
- 2