Tsukasa Kusakabe — accepted AI-conference papers

2022

Probabilistic Approach to Online Stiffness Estimation for Robotic Tasks

IROS 2022poster

Information about environmental stiffness is useful for robotic tasks involving interactions with unstructured and unknown environments. However, online estimation remains a challenge. Owing to the nature of its calculation algorithm, a large amount of noise may be generated, depending on the respon…

Cited by 3 Source ↗Scholar ↗

2021

Reinforcement Learning for Robotic Assembly Using Non-Diagonal Stiffness Matrix

Masahide Oikawa, Tsukasa Kusakabe, Kyo Kutsuzawa, Sho Sakaino, Toshiaki Tsuji

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 49 Source ↗Scholar ↗