Yasuaki Kuroe

About

Yasuaki Kuroe is a leading researcher in robotics, neural networks, and geometric algebra, with a career marked by innovative approaches to complex control and learning problems. His most influential work, "Applications of Clifford’s Geometric Algebra" (2013), has garnered 169 citations, showcasing his foundational contributions to mathematical frameworks for robotics and computer vision. Kuroe has made major strides in robot manipulator control, notably developing neural network methods for solving inverse kinematics—enabling robots to learn both position and velocity mappings from task space to joint space. His work on decoupling control using variable-structure disturbance observers (2002) advanced precision in robotic manipulation. In multi-robot systems, Kuroe pioneered swarm reinforcement learning methods (2013, 2015), where multiple agents collaborate to solve formation problems, improving learning certainty and adaptability. He also introduced model-inclusive learning for neural networks, applied to shape-from-shading and motion field estimation, bridging computer vision and robotics. With a portfolio of papers addressing inverse kinematics, smoothness-preserving mappings, and continuous state-action spaces, Kuroe’s research has consistently pushed boundaries in intelligent robotics and autonomous systems, earning him recognition as a key figure in applied neural network and control theory.

Research Focus

Key Achievements

Top Papers

1. 1
   Applications of Clifford’s Geometric Algebra

   169 citations · 2013
2. 2
   A new neural network learning of inverse kinematics of robot manipulator

   23 citations · 2002
3. 3
   Swarm reinforcement learning methods improving certainty of learning for a multi-robot formation problem

   21 citations · 2015
4. 4
   Swarm Reinforcement Learning Method for a Multi-robot Formation Problem

   15 citations · 2013
5. 5
   Decoupling control of robot manipulators by using variable-structure disturbance observer

   11 citations · 2002
6. 6
   Swarm reinforcement learning methods for problems with continuous state-action space

   9 citations · 2011
7. 7
   Versatile neural network method for recovering shape from shading by model inclusive learning

   7 citations · 2011
8. 8
   Estimation Method of Motion Fields from Images by Model Inclusive Learning of Neural Networks

   6 citations · 2009
9. 9
   A new neural network approach to the inverse kinematics problem in robotics

   5 citations · 2002
10. 10
    A neural network learning of nonlinear mappings with considering their smoothness and its application to inverse kinematics

    3 citations · 2002

Key Collaborators

Contact & Links