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Kyo Kutsuzawa

3 accepted papers

2025

Two-stage Learning Framework Combining Joint-level Reinforcement Learning and Muscle-level Adaptation for Musculoskeletal Locomotion

IROS 2025

Animal musculoskeletal systems are renowned for their ability to dynamically regulate stiffness and achieve energy-efficient motion. Being inspired by the biological control structure, this study presents a hybrid control framework that utilizes two-stage learning processes for body movement plannin

Cited by 0SourceScholar
2021

Reinforcement Learning for Robotic Assembly Using Non-Diagonal Stiffness Matrix

RA-L 2021

Contact-rich tasks, wherein multiple contact transitions occur in a series of operations, have been extensively studied for task automation. Precision assembly, a typical example of contact-rich tasks, requires high time constants to cope with the change in contact state. Therefore, this letter prop

Cited by 49SourceScholar
2018

Sequence-to-Sequence Model for Trajectory Planning of Nonprehensile Manipulation Including Contact Model

RA-L 2018

Nonprehensile manipulation is necessary for robots to operate in humans' daily lives. As nonprehensile manipulation should satisfy both kinematics and dynamics requirements simultaneously, it is difficult to manipulate objects along given paths. Previous studies have considered the problems with seq

Cited by 15SourceScholar