Max Robotics — Global Service Robot Directory + US Certification

About

Taylor Kessler Faulkner is a leading researcher at the intersection of human-robot interaction, physically assistive robotics, and interactive machine learning. Her work focuses on creating robots that can safely and effectively collaborate with people, particularly those with mobility impairments. Her most impactful contribution is the development of an adaptable, safe, and portable robot-assisted feeding system (2024, 19 citations), which empowers users to feed themselves by mounting directly onto powered wheelchairs with comprehensive safety checks. Faulkner has also pioneered algorithms for more intuitive human-robot teaching, including Active Attention-Modified Policy Shaping (2019, 9 citations), which allows robots to request feedback from multi-tasking humans without constant interruption. Her foundational work on modeling human beliefs for effective help-seeking (2018, 5 citations) and policy shaping with supervisory attention (2018, 5 citations) has advanced how robots learn from non-expert users. Beyond technical contributions, Faulkner critically examines research practices, notably highlighting the underrepresentation of target disability populations in physically assistive robotics studies (2024). Her work on Human-Interactive Robot Learning (2022) provides a comprehensive framework for designing teachable robotic agents, cementing her role as a key voice in making assistive robots both more capable and more inclusive.