Shigenobu Kobayashi

About

Shigenobu Kobayashi is a pioneering researcher in the field of reinforcement learning for robotics, with a particular focus on enabling complex locomotion in legged and nonholonomic systems. His most influential work, "Reinforcement learning of walking behavior for a four-legged robot" (2002, 28 citations), introduced an innovative actor-critic algorithm that successfully navigated an eight-dimensional continuous state/action space, allowing a quadruped robot to learn walking gaits through trial and error. Kobayashi further advanced the field by developing instance-based policy learning methods using real-coded genetic algorithms (2009, 13 citations), which provided a practical solution for controlling nonholonomic systems where theoretical derivations often fail. His research also tackled multi-criteria reinforcement learning for bipedal walking (2005, 9 citations) and real-time learning for ring robots with incomplete perception (2003). By combining stochastic gradient algorithms with direct policy search, Kobayashi demonstrated that robots could acquire sophisticated control policies without requiring mathematical models of their dynamics. His work has been instrumental in bridging the gap between theoretical reinforcement learning and practical robotic applications, inspiring subsequent generations of researchers to apply these techniques to increasingly complex real-world systems.

Research Focus

Key Achievements

Top Papers

1. 1
   Reinforcement learning of walking behavior for a four-legged robot

   28 citations · 2002
2. 2
   Instance-based Policy Learning by Real-coded Genetic Algorithms and Its Application to Control of Nonholonomic Systems

   13 citations · 2009
3. 3
   Reinforcement Learning using Stochastic Gradient Algorithm and its Application to Robots

   11 citations · 1999
4. 4
   Multi Criteria Reinforcement Learning Based on Goal-directed Exploration and its Application to Bipedal Walking Robot

   9 citations · 2005
5. 5
   A Policy Representation Using Weighted Multiple Normal Distribution Real-time Reinforcement Learning Feasible for Varying Optimal Actions.

   4 citations · 2003
6. 6
   Learning in robotics. Search and Learning by Genetic Algorithms.

   4 citations · 1995
7. 7
   Optimization of Instance-based Policy Based on Real-coded Genetic Algorithms

   3 citations · 2008
8. 8
   Reinforcement Learning for Crawling Robot Motion Using Stochastic Gradient Ascent

   2 citations · 1999

Key Collaborators

Contact & Links