Max Robotics — Global Service Robot Directory + US Certification

About

Simon Holk is an emerging researcher working at the intersection of human-robot interaction, reinforcement learning, and multisensory perception. His work spans two compelling research threads: understanding how humans perceive and respond to social robots and avatars, and developing more efficient methods for teaching robots through human feedback. Holk's most notable contribution lies in preference-based reinforcement learning, where he has pioneered multiple frameworks designed to reduce the burden of human labeling in robot training. His papers — including PREDILECT, POLITE, VARIQuery, and SEQUEL — collectively address one of the field's central challenges: how to extract meaningful robot behavior from limited and noisy human input. By incorporating zero-shot language reasoning, semi-supervised learning, and active query selection, these works have collectively garnered over 40 citations since 2023, reflecting their timely relevance to the robotics community. Beyond robot learning, Holk's research on the "Smiling McGurk Effect" (14 citations) demonstrates a sophisticated interest in how multisensory signals shape emotional perception in human-avatar and human-robot communication. Together, his contributions paint the picture of a researcher dedicated to making robots not only more learnable, but more naturally attuned to human expression and preference.