Sakmongkon Chumkamon
Guangdong University of Technology, Kyushu Institute of Technology
Papers
8
Total Citations
77
H-Index
5
About
Sakmongkon Chumkamon is a robotics researcher whose work bridges human-robot interaction, emotion-aware systems, and autonomous manipulation. His primary research areas include collaborative robotics, deep learning for motion generation, and socially intelligent robots capable of recognizing and expressing emotions. Chumkamon’s most cited work, "Collaborative Human-Robot Motion Generation Using LSTM-RNN" (29 citations), introduces a deep learning method that enables robots to predict and adapt to human motion during handovers, significantly improving responsiveness in shared workspaces. He has also made notable contributions to companion robotics, developing architectures that combine topological consciousness and adaptive resonance theory to generate intelligent emotional behaviors (21 citations). His research extends to facial expression recognition using constrained local models and Hidden Markov models, as well as long-term robot autonomy through anomaly classification and recovery policies. Chumkamon’s work on collision-aware AR telemanipulation and voice-controlled assistive robots further demonstrates his commitment to practical, human-centered robotics. With a career spanning foundational interaction models to cutting-edge teleoperation interfaces, his research continues to shape how robots perceive, respond to, and collaborate with humans in dynamic environments.
Research Focus
Key Achievements
Top Papers
- 1Collaborative Human-Robot Motion Generation Using LSTM-RNN29 citations · 2018
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- 5Collision-Aware AR Telemanipulation Using Depth Mesh5 citations · 2022
- 6The robot's eye expression for imitating human facial expression5 citations · 2014
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- 8Robot Assisting Water Serving to Disabilities by Voice Control2 citations · 2021