Ryuki Tachibana
Papers
6
Total Citations
474
H-Index
6
About
Ryuki Tachibana is a leading researcher at the intersection of deep reinforcement learning and high-precision industrial robotics. His work directly tackles one of manufacturing’s most stubborn challenges: enabling robots to perform assembly tasks with accuracy that exceeds their own mechanical precision. Tachibana’s most influential contribution, “Deep reinforcement learning for high precision assembly tasks” (300 citations), demonstrates how a robot can master the classic peg-in-hole problem through trial-and-error learning, eliminating the need for tedious manual parameter tuning. To make such learning viable in the real world, he developed OptLayer, a practical constrained optimization framework for deep RL (118 citations) that prevents the unsafe, unconstrained motions typical of simulated agents. This innovation bridges the gap between simulated training and real-world deployment. His work also extends to human-like motion prediction using Long Short-Term Memory networks and the creation of experimental force-torque datasets for multi-shape insertion tasks. By systematically replacing hand-crafted control logic with learned policies, Tachibana is paving the way for flexible, autonomous manufacturing systems that can adapt to new tasks without extensive reprogramming.
Research Focus
Key Achievements
Top Papers
- 1Deep reinforcement learning for high precision assembly tasks300 citations · 2017
- 2
- 3Deep Reinforcement Learning for High Precision Assembly Tasks29 citations · 2017
- 4
- 5Human-Like Hand Reaching by Motion Prediction Using Long Short-Term Memory10 citations · 2017
- 6