Hideaki Ito
Papers
2
Total Citations
5
H-Index
2
About
Hideaki Ito is a robotics researcher whose work focuses on advancing the capabilities of life-sized humanoid robots, particularly in the domains of whole-body control and tool manipulation. His major contributions lie in two key areas: enabling robots to learn complex force-adjustment skills through deep reinforcement learning, and developing robust whole-body control strategies for maintaining stability under multi-contact scenarios. In his 2020 study on tool force adjustment, Ito introduced a novel framework that combines deep reinforcement learning with an active teaching request mechanism, allowing a humanoid robot to acquire intricate manipulation skills—such as handling tools requiring precise force modulation—without relying on explicit physical models. His earlier 2019 work on whole-body control addresses the critical challenge of managing contact wrench constraints and joint overload in 3D environments, proposing a method that distributes internal wrenches and leverages self-collision to reduce joint stress. While his citation counts (3 and 2, respectively) reflect early-stage impact, these papers represent foundational steps toward more dexterous and physically robust humanoid robots. Ito’s research is particularly notable for its practical focus on position-controlled platforms, bridging the gap between simulation and real-world deployment in industrial or assistive robotics.
Research Focus
Key Achievements
Top Papers
- 1
- 2