Bulent Tastan

Papers

Total Citations

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About

Bulent Tastan is a researcher whose work lies at the intersection of human-robot interaction and multi-robot systems, with a particular focus on adaptive autonomy and operator support. His key contributions address the fundamental challenge of balancing human control with robotic autonomy in complex, real-world scenarios. In his most cited work, "An adjustable autonomy paradigm for adapting to expert-novice differences" (2013, 11 citations), Tastan pioneered a framework that dynamically adjusts the level of robot autonomy based on the operator's expertise, significantly improving performance in multi-robot manipulation tasks. This work is notable for recognizing that one-size-fits-all autonomy models fail when operators range from novices to experts. Building on this, his research on "Improving multi-robot teleoperation by inferring operator distraction" (2010, 5 citations) introduced methods to detect when an operator's cognitive load is too high, allowing the system to compensate by increasing robot autonomy. These contributions are particularly impactful for search and rescue operations, where fully autonomous systems are unreliable, but human operators are easily overwhelmed. Tastan's work demonstrates a deep understanding of the cognitive demands of teleoperation, paving the way for more intuitive and effective human-robot teams in high-stakes environments.