Daehyung Park is a Ph.D. in the College of Computing and the Institute for Robotics and Intelligent Machines (IRIM) at the Georgia Institute of Technology, working with Dr. Charles C. Kemp in the Healthcare Robotics Laboratory. He is currently researching assistive manipulation and multimodal execution monitoring methods for people with disabilities. 

Park has experience in a broad range of robotics in both academic and industrial areas. He received a B.S. at Osaka University in Japan, where he researched HRP-2' turning motion (advisor: Dr. Arai Tatsuo). He received an M.S. at the University of Southern California, where he researched movement primitives for articulated manipulators (advisor: Dr. Stefan Schaal). After earning his master’s degree, he worked as a robotics researcher at the Mechatronics R&D Center of Samsung Electronics Inc.

Research Interests  -  Manipulation, Machine Learning, Perception

  • D. Park et al., "Active Feeding System using a General-purpose Manipulator," International Symposium on Medical Robotics (ISMR), 2018  
  • Successfully defended my dissertation, "A Multimodal Execution Monitor for Assistive Robots" !!!
  • D. Park, H. Kim, and C. C. Kemp. “Multimodal Anomaly Detection for Assistive Robots”, Autonomous Robots, 2018 (Conditionally accepted)
  • D. Park, Y. Hoshi, and C. C. Kemp. “A Multimodal Anomaly Detector for Robot-Assisted Feeding Using an LSTM-based Variational Autoencoder”, IEEE Robotics and Automation Letters (RA-L), 2018
  • My IROS 2017 video in IEEE spectrum's Video Friday
Recent Projects
A Multimodal Execution Monitor

We introduce a new execution monitor that detects and classifies anomalies using multimodal sensory signals for manipulation tasks. The monitor models the spatiotemporal dynamics of the sensor information using a generative model (i.e., hidden Markov models or long short-term memory networks). The monitor detects an anomaly when current anomaly score is higher than a state-based threshold. The monitor then classifies the type and cause of an anomaly using mutilayer perceptron (MLP).


Robot-Assisted Feeding System

We present a proof-of-concept robotic system for assistive feeding using a general-purpose mobile manipulator for people with disabilities.


Haptically-guided Mapping, Planning, and Control

In this work, we are focusing on methods for haptic mapping, planning, and control during reaching into the unknown environment.