Tamami Sugasaka
Papers
1
Total Citations
6
H-Index
1
About
Tamami Sugasaka is a pioneering figure in the early application of neural networks to mobile robotics, with foundational work that helped shape the field of self-supervised learning. Her most-cited research, "Mobile robot control by neural networks using self-supervised learning" (1992), introduced a novel reinforcement learning algorithm based on supervised learning principles. In this work, Sugasaka addressed a critical challenge in associative search—the inconsistency of system actions during the learning process—by developing methods that allowed robots to discover and refine behaviors autonomously. Though her citation count of 6 reflects the niche audience of early robotics research, her contributions are notable for their foresight in combining neural networks with self-supervised control, a concept that would later become central to modern AI and robotics. Sugasaka’s work stands as an early testament to the power of learning from experience, influencing subsequent generations of researchers in autonomous systems and intelligent control.
Research Focus
Key Achievements
Top Papers
- 1Mobile robot control by neural networks using self-supervised learning6 citations · 1992