Papers
26
Total Citations
585
H-Index
12
About
Daehyung Park is a robotics researcher whose work sits at the intersection of assistive robotics, multimodal sensing, anomaly detection, and human-robot interaction. He is perhaps best known for his pioneering contributions to multimodal execution monitoring, developing systems that enable robots to detect and classify anomalies during manipulation tasks using complementary sensory streams — work that has accumulated over 200 citations across several influential papers. His research on robot-assisted feeding exemplifies his commitment to translating theoretical advances into real-world assistive technology, demonstrating how semi-autonomous systems can meaningfully improve the quality of life for people with disabilities. Park has also made significant contributions to semantic grounding and instruction following, enabling robots to interpret natural language commands even under conditions of incomplete world knowledge. His 2018 LSTM-based variational autoencoder for anomaly detection reflects his facility with deep learning architectures applied to safety-critical robotic contexts. More recently, his highly cited 2024 survey on integrating large language models with intelligent robots (89 citations) signals his engagement with the frontier of AI-robotics convergence. Across his career, Park's research consistently prioritizes robust, safe, and human-centered robotic systems capable of operating reliably in complex, real-world environments.
Research Focus
Key Achievements
Top Papers
- 1A survey on integration of large language models with intelligent robots89 citations · 2024
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- 4Multimodal anomaly detection for assistive robots47 citations · 2018
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- 8Towards Assistive Feeding with a General-Purpose Mobile Manipulator33 citations · 2016
- 9Combining tactile sensing and vision for rapid haptic mapping23 citations · 2015
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