Ashley Feniello

Microsoft Research (United Kingdom), Microsoft (United States)

Papers

Total Citations

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About

Ashley Feniello is a researcher whose work sits at the intersection of robotics, computer vision, and human-robot interaction, with a particular focus on making autonomous systems more practical and intuitive. Her most cited paper, "Reliable Kinect-based navigation in large indoor environments" (2015, 16 citations), tackles a critical challenge in robotics: achieving robust, cost-effective navigation. While expensive laser range finders were the gold standard for accuracy, Feniello demonstrated that Microsoft’s low-cost Kinect sensor could be engineered to deliver reliable mapping and localization in large, complex indoor spaces, significantly lowering the barrier to entry for autonomous navigation research. Her second major contribution, "Program synthesis by examples for object repositioning tasks" (2014, 11 citations), addresses the problem of teaching robots through demonstration. Feniello introduced a novel stack-based domain-specific language (DSL) and a learning algorithm that synthesizes human-readable computer programs from simple human demonstrations, enabling robots to learn object manipulation tasks without requiring expert programming. This work bridges the gap between non-expert users and complex robotic systems, making robot programming more accessible. Together, these contributions highlight Feniello’s impact in democratizing robotics through affordable hardware and intuitive programming paradigms.