Alexander Skoglund
Papers
8
Total Citations
131
H-Index
6
About
Alexander Skoglund is a pioneering researcher in the field of robotics, specializing in Programming by Demonstration (PbD) for industrial manipulators. His work focuses on simplifying robot programming for non-experts by enabling robots to learn tasks through human demonstration. Skoglund’s major contributions include developing a PbD system that uses task primitives to automatically generate manipulator programs for pick-and-place tasks, as well as a wearable input device for position teaching that employs supervised learning. His research has garnered significant attention, with his most-cited paper, "Programming by Demonstration of Pick-and-Place Tasks for Industrial Manipulators using Task Primitives" (2007), accumulating 39 citations, and his early work on wearable input devices (2004) reaching 31 citations. Skoglund also advanced the field with a next-state-planner approach for reaching motions and an under-actuated anthropomorphic hand system for autonomous grasping, demonstrating real-world applications in simulation and experiments. His notable achievements include bridging the gap between human intuition and robotic precision, making industrial robotics more accessible. Skoglund’s work continues to inspire researchers in human-robot interaction and autonomous manipulation, laying the groundwork for intuitive, adaptive robotic systems.
Research Focus
Key Achievements
Top Papers
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- 4Programming by demonstration of robot manipulators12 citations · 2009
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- 7Towards a supervised dyna-Q application on a robotic manipulator3 citations · 2005
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