Papers
13
Total Citations
361
H-Index
8
About
Nick DePalma is a human-robot interaction (HRI) researcher whose work sits at the intersection of social robotics, machine learning, and crowdsourced data collection. He is perhaps best known for pioneering the use of online games and crowdsourcing platforms to generate large-scale corpora of human interaction data for training social robots — a methodological innovation that addressed one of HRI's most persistent bottlenecks. His landmark 2013 paper on crowdsourcing HRI in public environments (91 citations) demonstrated how diverse, real-world behavioral data could be harvested at scale, while earlier work from 2011 laid the conceptual groundwork by translating virtual interactions into physical robotic applications. DePalma has also made notable contributions to social learning in robotics, exploring how mechanisms like stimulus enhancement and emulation — beyond simple imitation — can computationally benefit robot learning. His 2013 study on "engaging robots" (75 citations) shed light on the importance of backchanneling and social signaling in human-robot teamwork. More recently, his 2024 paper on Large Language Models in HRI (42 citations) demonstrates his continued relevance, critically examining the ethical and practical boundaries of deploying LLMs on interactive robots. Across more than 300 cumulative citations, DePalma's work has meaningfully advanced how robots learn from and communicate with humans.
Research Focus
Key Achievements
Top Papers
- 1
- 2Engaging robots75 citations · 2013
- 3
- 4Scarecrows in Oz: The Use of Large Language Models in HRI42 citations · 2024
- 5Exploiting social partners in robot learning36 citations · 2010
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- 7
- 8Effects of social exploration mechanisms on robot learning9 citations · 2009
- 9
- 10Leveraging Online Virtual Agents to Crowdsource Human-Robot Interaction6 citations · 2011