Max Robotics — Global Service Robot Directory + US Certification

About

Ryunosuke Yokoya’s research lies at the intersection of developmental robotics, imitation learning, and cognitive modeling, exploring how robots can learn from and interact with humans and their environment. His most influential work centers on the use of Recurrent Neural Networks with Parametric Bias (RNNPB) to enable robots to imitate human motions despite significant differences in morphology. His 2007 paper, “Experience-based imitation using RNNPB,” which has garnered 29 citations, is a cornerstone in this area, proposing a framework where robots leverage past experiences to generate novel, adaptive movements. Yokoya further advanced the field by investigating how robots can discover and model “other individuals” through self-projection, a concept detailed in his 2007 work on projecting a self-model for imitation, which has received 11 citations. He also contributed to object manipulation by developing techniques for motion generation based on reliable predictability, as seen in his 2008 paper with 11 citations. Through his focused body of work, Yokoya has provided foundational insights into how robots can autonomously learn, predict, and imitate, bridging the gap between self-modeling and social interaction in artificial systems.