Staffan Ekvall

KTH Royal Institute of Technology

Papers

18

Total Citations

1,048

H-Index

14

About

Staffan Ekvall is a robotics researcher whose work sits at the intersection of robot learning, manipulation, and autonomous perception. His most significant contributions center on programming by demonstration — a paradigm that enables robots to acquire complex skills by observing human actions rather than relying on explicit preprogramming. Ekvall's foundational papers on grasp recognition and interactive grasp learning, together accumulating nearly 250 citations, introduced methods using hidden Markov models and human-guided demonstrations to teach robots dexterous manipulation. His 2007 work on automatic grasp generation further advanced the field by combining experiential learning with geometric shape primitives, earning over 110 citations. Beyond manipulation, Ekvall made notable strides in mobile robotics, developing approaches that integrate object recognition with simultaneous localization and mapping (SLAM), enabling service robots to reason semantically about their environments. His task-level planning research addressed real-world challenges like domestic robot deployment, where rigid preprogramming falls short. Across ten highly cited publications spanning 2004–2008, his work collectively reflects a sustained effort to make robots more adaptable, perceptive, and capable of learning naturally from human partners — contributions that remain influential in human-robot interaction and cognitive robotics research.

Research Focus

Key Achievements

14
H-Index
18
Papers
1,048
Total Citations
58
Avg Citations/Paper
🏆 Most Cited Paper
Grasp Recognition for Programming by Demonstration
124 citations · 2006
📈 Most Prolific Year: 2006 (7 Papers)
🤝 Key Collaborators: 7
🏛 Institutions: KTH Royal Institute of Technology

Top Papers

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Key Collaborators

Contact & Links

Available for collaboration
Content generated · 16 days ago