Kevin Lieberman
Papers
2
Total Citations
5
H-Index
2
About
Kevin Lieberman’s research lies at the intersection of multi-agent robotics and human-machine teaming, with a focus on decentralized control and trust calibration. His most-cited work, “Decentralized Control of Multi-Agent Escort Formation via Morse Potential Function” (2012, 3 citations), introduces a novel algorithm for entrapment/escorting missions—a critical capability for security and surveillance applications. By leveraging Morse potential functions, Lieberman enables a swarm of agents to autonomously form and maintain a protective formation around a mobile target, operating without centralized oversight. This decentralized approach enhances scalability and robustness in dynamic environments. In his more recent work, “A Comparison of Auditory and Visual Representations of System Confidence to Support Trust Specificity, Attention Management, and Joint Performance in Human-Machine Teams” (2021, 2 citations), Lieberman tackles the pressing challenge of trust miscalibration in human-robot teams. He demonstrates how different sensory modalities for conveying system confidence can improve operators’ attention allocation and joint performance, reducing misuse or disuse of technology. Though early in his career, Lieberman’s contributions are shaping safer, more effective human-robot collaborations.
Research Focus
Key Achievements
Top Papers
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- 2