Masa-aki Sato

About

Masa-aki Sato has made pioneering contributions at the intersection of machine learning and robotics, particularly in developing algorithms for adaptive control and biped locomotion. His most influential work, the "On-line EM Algorithm for the Normalized Gaussian Network" (2000, 280 citations), introduced a powerful method for training local linear regression models that softly partition input space—a foundational technique in online learning and neural network research. Building on this, Sato’s research shifted toward biologically inspired robotics, where he became a leader in applying reinforcement learning to Central Pattern Generator (CPG)-based controllers for biped robots. His 2007 paper on a "CPG-actor-critic method" (118 citations) and related works (2004–2006, collectively over 200 citations) demonstrated how policy gradient methods could efficiently tune CPG parameters, enabling robust, adaptive locomotion without manual tuning. Sato also tackled classic control challenges, such as balancing the Acrobot (2003, 19 citations), showcasing the versatility of his reinforcement learning approaches. With over 600 total citations across his key papers, Sato’s work has profoundly influenced both theoretical machine learning and practical robotics, inspiring subsequent research in online learning, neural networks, and autonomous locomotion systems.

Research Focus

Key Achievements

Top Papers

1. 1
   On-line EM Algorithm for the Normalized Gaussian Network

   280 citations · 2000
2. 2
   Reinforcement learning for a biped robot based on a CPG-actor-critic method

   118 citations · 2007
3. 3
   Learning CPG-based biped locomotion with a policy gradient method

   98 citations · 2006
4. 4
   Reinforcement learning for a CPG-driven biped robot

   66 citations · 2004
5. 5
   Reinforcement Learning for Biped Locomotion

   35 citations · 2002
6. 6
   Learning Sensory Feedback to CPG with Policy Gradient for Biped Locomotion

   30 citations · 2006
7. 7
   Application of reinforcement learning to balancing of Acrobot

   19 citations · 2003
8. 8
   Learning CPG-based biped locomotion with a policy gradient method

   10 citations · 2006

Key Collaborators

Contact & Links