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About

Inna Mikhailova is a robotics and cognitive systems researcher whose work centers on autonomous learning, human-robot interaction, and developmental robotics. Her research investigates how humanoid robots can acquire knowledge and build internal concepts through multimodal perception and interaction with human tutors, drawing inspiration from infant cognitive development. Mikhailova's most significant contribution is her development and iterative refinement of ALIS (Autonomous Learning and Interaction System), implemented on Honda's ASIMO humanoid robot. Across successive versions, ALIS integrated visual processing — including depth perception, planar surface detection, and motion analysis — with auditory capabilities such as speech recognition and sound localization, enabling robots to learn semantic associations in real time. Her 2008 paper introducing ALIS 2 remains her most cited work, with 23 citations, while subsequent studies on multimodal association learning and headset-free speech interaction each garnered 13 and 9 citations respectively. Her broader research agenda addresses open-ended, incremental system design in developmental robotics — exploring how machines can autonomously determine not just how to learn, but what to learn and when. This work places Mikhailova at the intersection of machine learning, cognitive science, and autonomous robotics, contributing foundational ideas to the challenge of building genuinely adaptive, self-directed robotic systems.