Shizen Ohnishi
Papers
1
Total Citations
9
H-Index
1
About
Shizen Ohnishi is a pioneering researcher in autonomous robotics and machine learning, with a focus on instance-based reinforcement learning for robot navigation and behavior acquisition. His most-cited work, "Instance-based reinforcement learning for robot path finding in continuous space" (2002, 9 citations), introduces two innovative methods for shaping autonomous mobile robots: an instance-based classifier generator for learning primitive behaviors, and a reinforcement learning approach based on behavior sequence memory. This foundational contribution addresses the challenge of enabling robots to learn and adapt in continuous, unstructured environments without requiring pre-programmed models. Ohnishi’s research bridges the gap between reinforcement learning theory and practical robotics, emphasizing real-time learning and memory-based decision-making. His work has influenced subsequent studies in robot path planning and behavior learning, particularly in scenarios where robots must generalize from limited experiences. By integrating instance-based learning with reinforcement learning, Ohnishi has provided a framework that allows robots to efficiently acquire and refine complex behaviors through interaction with their environment. His contributions remain relevant for researchers exploring adaptive autonomy and lifelong learning in robotics.
Research Focus
Key Achievements
Top Papers
- 1