David Hoying
Papers
1
Total Citations
3
H-Index
1
About
David Hoying is a researcher at the intersection of robotics, computer vision, and human-robot interaction, with a focus on enabling robots to perform complex, real-world manipulation tasks by learning from human demonstrations. His most-cited work, "Robot Learning to Mop Like Humans Using Video Demonstrations" (2023), tackles the deceptively challenging problem of teaching robots to perform domestic chores—specifically mopping—with human-like adaptability. Rather than hand-coding behaviors for every possible surface or mess, Hoying’s system leverages video demonstrations to allow robots to generalize mopping strategies across variable environments, a significant step toward practical home robotics. While his citation count is still growing, this work has been recognized for its novel approach to bridging the gap between scripted robot actions and the nuanced, context-aware movements humans naturally employ. Hoying’s contributions highlight a broader push toward robots that can learn from unstructured, everyday data, making them more accessible and useful in domestic settings. His research is particularly relevant for students and engineers interested in imitation learning, task generalization, and the future of service robotics.
Research Focus
Key Achievements
Top Papers
- 1Robot Learning to Mop Like Humans Using Video Demonstrations3 citations · 2023