About

Manuela Veloso is a pioneering researcher in artificial intelligence, robotics, and multiagent systems, whose work has fundamentally shaped how autonomous robots learn, navigate, and collaborate. Best known for her foundational contributions to robot learning from demonstration — a survey on which has accumulated over 3,250 citations — Veloso established key frameworks for enabling robots to acquire complex behaviors by observing human actions. Her influential survey on multiagent systems from a machine learning perspective (1,188 citations) helped define the theoretical landscape for cooperative and competitive AI agents, while her co-authorship of Science Robotics' grand challenges (1,134 citations) reflects her stature as a visionary in shaping the field's long-term research agenda. Veloso's applied contributions are equally impressive, spanning indoor robot navigation using depth cameras and WiFi localization, real-time color image segmentation, randomized path planning, and activity recognition using conditional random fields. Through her leadership of Carnegie Mellon's CoBots — autonomous service robots operating in real-world office environments — she demonstrated practical, deployable AI systems years before such ambitions became mainstream. Across decades of research, Veloso has consistently bridged theoretical rigor with real-world impact, making her one of the most influential figures in modern robotics and autonomous systems.

Research Focus

Key Achievements

53
H-Index
340
Papers
17,188
Total Citations
51
Avg Citations/Paper
🏆 Most Cited Paper
A survey of robot learning from demonstration
3,252 citations · 2008
📈 Most Prolific Year: 2018 (22 Papers)
🤝 Key Collaborators: 326
🏛 Institutions: Carnegie Mellon University, Laboratoire d'Informatique de Paris-Nord, Morgan Stanley (United States), Boğaziçi University, Universidade Paranaense, Nanyang Technological University

Top Papers

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Key Collaborators

Contact & Links

Available for collaboration
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