M. Jeambrun

Papers

Total Citations

H-Index

About

M. Jeambrun is a pioneering researcher at the intersection of humanoid robotics and social cognition, whose work explores how humans can intuitively command, interrogate, and teach robots through natural social interaction. Their most-cited paper (2006, 24 citations) established foundational frameworks for designing robots that leverage spoken language and theories of intentionality, enabling more natural human-robot collaboration. Jeambrun’s contributions address the critical challenge of making complex humanoid systems accessible to non-expert users, bridging gaps between engineering, cognitive science, and linguistics. By integrating insights from social cognition, they have helped shape how robots interpret human gestures, speech, and intent—paving the way for more responsive and teachable machines. Though their citation count reflects a focused but impactful body of work, Jeambrun’s influence extends into broader discussions on human-robot interaction design and the ethical implications of socially intelligent machines. Their research remains essential reading for students and scholars interested in building robots that are not just powerful, but genuinely collaborative partners.