Daehyung Park is a Postdoc in Computer Science and Artificial Intelligence Laboratory at Massachusetts Institute of Technology, working with Dr. Nicholas Roy in Robust Robotics Group. He is currently researching manipulation and natural language processing.

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. He received a Ph.D. at Georgia Institute of Technology, where he researched a multimodal execution monitor for assistive robots (advisor: Dr. Charles C. Kemp).


Research Interests  -  Manipulation, Machine Learning, Perception


News
  • My feeding system is exposed on media, Mouser and Grant Imahara, March 2018
  • D. Park, H. Kim, and C. C. Kemp. “Multimodal Anomaly Detection for Assistive Robots”, Autonomous Robots, 2018
  • 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, 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).



feeding_feature

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.



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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.