Michael Gentner
Papers
3
Total Citations
55
H-Index
2
About
Michael Gentner is a robotics researcher whose work lies at the intersection of perception, manipulation, and human-robot collaboration. His primary research areas include active visuo-tactile perception, object pose estimation, and proactive task planning in cluttered and collaborative environments. Gentner’s major contribution is a novel framework that integrates vision and tactile sensing to enable robots to accurately estimate the SE(3) pose of objects in dense clutter. His 2022 paper on this topic, which has garnered 41 citations, introduces a “declutter graph” to model object relationships and guide interactive exploration. This work addresses a critical bottleneck in autonomous manipulation: reliable perception when objects are stacked or occluded. In a related 2021 study (13 citations), he extended this approach to unknown workspaces, demonstrating robust point cloud registration guided by tactile feedback. More recently, Gentner has advanced human-robot collaboration by developing proactive task sequencing methods that predict human hand motion in real time, moving beyond reactive planning to anticipate user intent. His research is highly relevant for industrial automation and assistive robotics, offering practical solutions for safe, efficient, and adaptive robotic systems in real-world settings.
Research Focus
Key Achievements
Top Papers
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