Lasse Einig

Papers

Total Citations

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About

Lasse Einig’s research sits at the intersection of human-robot interaction, robotic manipulation, and intelligent planning systems, with a focus on making robots more intuitive, adaptive, and efficient in real-world tasks. His early work on *Immersive Remote Grasping* (2016, 12 citations) explored how emerging UI technologies and robotic systems can reshape human-robot collaboration, tackling the critical challenge of designing interfaces that are both reliable and comfortable for operators. In parallel, his study on *Learning Human Compliant Behavior from Demonstration* (2016, 11 citations) advanced the field of force-based robot manipulation by enabling robots to learn physical interaction skills from human demonstrations—a key step toward autonomous service robots that can safely handle contact-rich tasks. Einig also contributed to system-level efficiency with his work on *Parallel Plan Execution and Re-planning* (2013, 5 citations), which integrated state machines with Hierarchical Task Network planning to allow mobile robots to execute actions concurrently and adapt dynamically, reducing idle time and improving resource use. Though his citation counts are modest, Einig’s contributions address foundational challenges in making robots more responsive and collaborative, particularly in domains requiring physical interaction and adaptive planning.