Daichi Saito

Mr. Daichi Saito

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Research Focus & Metrics

Academic Career Length

19Years (2008-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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12 February 2008 in Hydrobiologia
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Journal Article
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SHSayumi Hoshino
Sayumi Hoshino
DSDaichi Saito
Daichi Saito
TFToshihiko Fujita
Toshihiko Fujita

5 June 2023 in Machine Vision and Applications
Verified
Journal Article
Closed access
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NWNaoki Wake
Naoki Wake
DSDaichi Saito
Daichi Saito
KSKazuhiro Sasabuchi
Kazuhiro Sasabuchi
HKHideki Koike
Hideki Koike
KIKatsushi Ikeuchi
Katsushi Ikeuchi

19 July 2018 in SynOpen
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Journal Article
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KMKenichi Maruyama
Kenichi Maruyama
DSDaichi Saito
Daichi Saito
KMKoichi Mikami
Koichi Mikami
Dedicated to Professor V. Snieckus on the occasion of his 80th birthday. Difluoromethylation of the C9-H site of the fluorene ring using lithium base and fluoroform (CF3H), which is one of the most cost-effective difluoromethylating reagents, is attained to give difluoromethylated fluorenes with an all-carbon quaternary center. The Ruppert–Prakash reagent (CF3TMS) can also be applied to the present reaction system, providing siladifluoromethylated fluorenes that can be utilized for sequential carbon–carbon bond-forming reactions through activation of the silyl group.

27 December 2023 in Robotics
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Journal Article
Open access
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DSDaichi Saito
Daichi Saito
KSKazuhiro Sasabuchi
Kazuhiro Sasabuchi
NWNaoki Wake
Naoki Wake
AKAtsushi Kanehira
Atsushi Kanehira
JTJun Takamatsu
Jun Takamatsu
KIKatsushi Ikeuchi
Katsushi Ikeuchi
Robot manipulation in a physically constrained environment requires compliant manipulation. Compliant manipulation is a manipulation skill to adjust hand motion based on the force imposed by the environment. Recently, reinforcement learning (RL) has been applied to solve household operations involving compliant manipulation. However, previous RL methods have primarily focused on designing a policy for a specific operation that limits their applicability and requires separate training for every new operation. We propose a constraint-aware policy that is applicable to various unseen manipulations by grouping several manipulations together based on the type of physical constraint involved. The type of physical constraint determines the characteristic of the imposed force direction; thus, a generalized policy is trained in the environment and reward designed on the basis of this characteristic. This paper focuses on two types of physical constraints: prismatic and revolute joints. Experiments demonstrated that the same policy could successfully execute various compliant manipulation operations, both in the simulation and reality. We believe this study is the first step toward realizing a generalized household robot.