Jochen J. Steil
Papers
119
Total Citations
3,061
H-Index
30
About
Jochen J. Steil is a distinguished robotics and machine learning researcher whose work sits at the intersection of robot learning, motor control, and human-robot interaction. Best known for pioneering the concept of "goal babbling" — a biologically inspired approach to learning inverse kinematics without expert knowledge — his 2010 paper on the topic has garnered 170 citations and fundamentally influenced how redundant robotic systems acquire motor skills. This work found compelling real-world application in his research on the Bionic Handling Assistant, a soft elephant trunk-inspired robot, where he developed both data-driven learning strategies (158 citations) and elegant constant-curvature kinematic models (134 citations). Steil's contributions extend across dexterous manipulation, having demonstrated platform-portable grasping strategies for sophisticated anthropomorphic hands (147 citations), and into human-robot interaction, with influential studies on kinesthetic teaching and multi-modal instruction (100 and 84 citations respectively). His broader portfolio addresses imitation learning, visual attention modeling, and hybrid analytical-data-driven control frameworks, reflecting a consistently integrative approach to robotics research. Through over a decade of sustained, high-impact output, Steil has shaped how modern robots learn, move, and collaborate meaningfully with humans.
Research Focus
Key Achievements
Top Papers
- 1Goal Babbling Permits Direct Learning of Inverse Kinematics170 citations · 2010
- 2Efficient Exploratory Learning of Inverse Kinematics on a Bionic Elephant Trunk158 citations · 2014
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- 7Task-level imitation learning using variance-based movement optimization86 citations · 2009
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- 9Multi-modal human-machine communication for instructing robot grasping tasks84 citations · 2003
- 10Hybrid Analytical and Data-Driven Modeling for Feed-Forward Robot Control †81 citations · 2017