Khurram Gulzar

Papers

Total Citations

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About

Khurram Gulzar is a researcher whose work lies at the fascinating intersection of robotics, human-robot interaction, and multi-agent communication. His primary research focus is on enabling robots to communicate with each other using non-verbal, human-like gestures—specifically pointing and deictic (indicating) gestures—to share information about objects in their environment. This is a critical step toward creating truly collaborative multi-robot systems. Gulzar’s major contributions include developing probabilistic optimization methods for robot pointing gestures, as detailed in his 2015 paper "See what I mean," which has garnered 8 citations. He further advanced this work by introducing temporal arm tracking and probabilistic object selection for more robust robot-to-robot interaction, as seen in his 2016 paper. His 2018 work on "Robot–Robot Gesturing for Anchoring Representations" (also with 8 citations) tackles the foundational problem of how robots can establish shared symbolic meanings for objects through gesture, a key challenge for decentralized systems. While his citation counts are modest, they reflect a focused and novel niche. Gulzar’s work is notable for pushing beyond traditional coded communication, exploring how robots can use body language to achieve a more intuitive and flexible form of collaboration, directly inspired by human communication.