Meher T. Shaikh

Papers

Total Citations

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About

Meher T. Shaikh explores the critical intersection of human-robot interaction and multi-objective optimization, focusing on how humans can effectively communicate complex intent to autonomous systems. Her research centers on developing intuitive interfaces and metrics that bridge the gap between human preferences and algorithmic decision-making. Shaikh's most influential work, "Design and Evaluation of Adverb Palette" (2017, 10 citations), introduces a novel graphical interface that allows users to express acceptable tradeoffs among competing performance objectives—a fundamental challenge in human-robot collaboration. She further advanced this concept in "Interactive multi-objective path planning through a palette-based user interface" (2016, 5 citations), demonstrating how humans can guide robot path-planning through supervisory control. Her 2020 paper, "A Measure to Match Robot Plans to Human Intent" (2 citations), addresses the crucial problem of detecting when a robot's plan no longer aligns with human intent, contributing to more resilient human-robot teams. Through her work, Shaikh has made significant strides in making autonomous systems more responsive to nuanced human preferences, particularly in path-planning scenarios where multiple competing objectives must be balanced.