Michael Chung
Papers
10
Total Citations
133
H-Index
6
About
Michael Chung is a leading researcher in human-robot interaction, with a primary focus on democratizing robot programming through crowdsourcing and imitation learning. His seminal work on "Accelerating imitation learning through crowdsourcing" (2014, 26 citations) pioneered methods to overcome the critical bottleneck of expensive human demonstrations, enabling robots to learn from large-scale, non-expert input. Chung further advanced this paradigm with "Robot Programming by Demonstration with Crowdsourced Action Fixes" (25 citations), introducing a framework where crowd workers can correct and refine robot actions post-demonstration. Expanding into service robotics, his highly cited work "How was Your Stay?" (26 citations) explored the novel application of robots for gathering customer feedback in hospitality, bridging technical development with real-world deployment. Chung's contributions also include a Bayesian developmental approach to imitation learning and autonomous question-answering systems for human-populated environments. His iterative, user-centered design philosophy—evident in his work on social robot programming and repair—has produced over 130 citations, establishing him as a key figure in making socially interactive robots more accessible, adaptable, and practically deployable.
Research Focus
Key Achievements
Top Papers
- 1Accelerating imitation learning through crowdsourcing26 citations · 2014
- 2
- 3Robot Programming by Demonstration with Crowdsourced Action Fixes25 citations · 2014
- 4
- 5A Bayesian Developmental Approach to Robotic Goal-Based Imitation Learning14 citations · 2015
- 6
- 7Designing information gathering robots for human-populated environments4 citations · 2015
- 8
- 9Exploring the Potential of Information Gathering Robots2 citations · 2015
- 10