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About

Alexander Clegg is a robotics and AI researcher whose work spans embodied AI, robot-assisted physical interaction, and neural scene representations. His research is perhaps best known for bridging the gap between simulation and real-world robotics, tackling challenges that range from dexterous manipulation to human-robot collaboration. Clegg's most-cited contribution, "iSDF" (2022, 125 citations), introduced a real-time neural signed distance field framework enabling robots to perceive and navigate complex environments with unprecedented efficiency. His early and sustained focus on robot-assisted dressing — spanning multiple papers from 2017 to 2020 — demonstrated how deep reinforcement learning and haptic feedback could enable robots to safely assist humans with garment manipulation, a deceptively difficult problem involving deformable materials and human comfort. This thread of work accumulated over 150 citations collectively, signaling its influence on the field. His contributions to embodied AI infrastructure, including co-authorship on Habitat 2.0 and the Adaptive Skill Coordination framework, reflect a commitment to building the platforms and benchmarks that the broader research community relies upon. His 2019 sim2real navigation study further shaped how researchers critically evaluate progress in simulated environments. Across his portfolio, Clegg consistently combines rigorous simulation with real-world applicability, making him a notable voice in modern robotics research.