M. Masubuchi

About

M. Masubuchi is a pioneering researcher in the field of robotics and reinforcement learning, with a primary focus on bridging the gap between simulated and real-world learning for autonomous agents. His major contributions center on enabling physical robots to acquire behaviors through real-time reinforcement learning in authentic, unstructured environments—a challenge that many researchers had previously avoided due to computational and temporal constraints. Masubuchi’s most cited work, "Realtime reinforcement learning for a real robot in the real environment" (2002, 18 citations), demonstrates a practical framework for a physical robot to learn directly from its surroundings, overcoming the limitations of simulation-based approaches. He further advanced this paradigm by showing how learned behaviors can be propagated from a virtual agent to a physical robot (2002, 9 citations), effectively reducing the time and cost of real-world training. His early foundational paper (1996, 7 citations) laid the groundwork for this line of inquiry. Masubuchi’s research is notable for its emphasis on real-world applicability, making him a key figure in the development of adaptive, autonomous robots capable of learning in dynamic environments.

Research Focus

Key Achievements

Top Papers

1. 1
   Realtime reinforcement learning for a real robot in the real environment

   18 citations · 2002
2. 2
   Propagating learned behaviors from a virtual agent to a physical robot in reinforcement learning

   9 citations · 2002
3. 3
   Reinforcement Learning for a Real Robot in a Real Environment.

   7 citations · 1996

Key Collaborators

Contact & Links