Papers
28
Total Citations
360
H-Index
12
About
Kevin Haninger is a robotics researcher whose work sits at the intersection of physical human-robot interaction, force control, and safe collaborative automation. His research addresses some of the most pressing challenges in deploying robots alongside humans in industrial settings, particularly for high-payload applications where safety and ergonomic performance are paramount. Haninger's most influential contributions include foundational work on passivity and impedance control for flexible-joint robots, which earned 42 citations and redefined how stability guarantees are formulated for interactive systems. His investigations into admittance control and collision detection for high-payload robots have opened new frontiers in human-robot collaboration, collectively attracting tens of citations across multiple venues. A recurring theme in his work is the integration of advanced control frameworks — including Model Predictive Control with Gaussian Processes and disturbance observer-based methods — to enable robots to respond intelligently to human intent and environmental contact. Beyond classical control theory, Haninger has explored learning-based approaches, including reinforcement learning for impedance adaptation and dynamic movement primitives, reflecting a commitment to flexible, data-driven robotics. With over 250 cumulative citations across his top papers, his research has meaningfully shaped the trajectory of collaborative robotics, making safer, more ergonomic human-robot teamwork increasingly viable in real industrial environments.
Research Focus
Key Achievements
Top Papers
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- 7Integrated Disturbance Observer-Based Robust Force Control22 citations · 2022
- 8Seamless Human–Robot Collaboration in Industrial Applications13 citations · 2024
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