Zobrazeno 1 - 10
of 1 211
pro vyhledávání: '"Mori, Hiroki"'
Publikováno v:
2021 IEEE International Conference on Robotics and Automation (ICRA), Xi'an, China, 2021, pp. 2205-2211
A method that enables an industrial robot to accomplish the peg-in-hole task for holes in concrete is proposed. The proposed method involves slightly detaching the peg from the wall, when moving between search positions, to avoid the negative influen
Externí odkaz:
http://arxiv.org/abs/2403.19946
Publikováno v:
IEEE Robotics and Automation Letters, vol. 8, issue 3, pp. 1834-1841, 2023
Anchor-bolt insertion is a peg-in-hole task performed in the construction field for holes in concrete. Efforts have been made to automate this task, but the variable lighting and hole surface conditions, as well as the requirements for short setup an
Externí odkaz:
http://arxiv.org/abs/2312.16438
While deep learning enables real robots to perform complex tasks had been difficult to implement in the past, the challenge is the enormous amount of trial-and-error and motion teaching in a real environment. The manipulation of moving objects, due t
Externí odkaz:
http://arxiv.org/abs/2309.12547
Autor:
Mori, Hiroki, Kimura, Shunya
Publikováno v:
Proc. Interspeech 2023 (2023) 3372-3376
As the phonetic and acoustic manifestations of laughter in conversation are highly diverse, laughter synthesis should be capable of accommodating such diversity while maintaining high controllability. This paper proposes a generative model of laughte
Externí odkaz:
http://arxiv.org/abs/2306.03465
Sufficiently perceiving the environment is a critical factor in robot motion generation. Although the introduction of deep visual processing models have contributed in extending this ability, existing methods lack in the ability to actively modify wh
Externí odkaz:
http://arxiv.org/abs/2206.14530
Autor:
Iizuka, Takahisa, Mori, Hiroki
Publikováno v:
IEEE Access 10 (2022)
This study investigated the effect of synthetic voice of conversational agent trained with spontaneous speech on human interactants. Specifically, we hypothesized that humans will exhibit more social responses when interacting with conversational age
Externí odkaz:
http://arxiv.org/abs/2205.00755
Autor:
Ando, Tomoki, Iino, Hiroto, Mori, Hiroki, Torishima, Ryota, Takahashi, Kuniyuki, Yamaguchi, Shoichiro, Okanohara, Daisuke, Ogata, Tetsuya
We propose a new method for collision-free planning using Conditional Generative Adversarial Networks (cGANs) to transform between the robot's joint space and a latent space that captures only collision-free areas of the joint space, conditioned by a
Externí odkaz:
http://arxiv.org/abs/2202.13062
Deep robot vision models are widely used for recognizing objects from camera images, but shows poor performance when detecting objects at untrained positions. Although such problem can be alleviated by training with large datasets, the dataset collec
Externí odkaz:
http://arxiv.org/abs/2202.10036
Autor:
Ando, Tomoki, Mori, Hiroki, Torishima, Ryota, Takahashi, Kuniyuki, Yamaguchi, Shoichiro, Okanohara, Daisuke, Ogata, Tetsuya
We show a new method for collision-free path planning by cGANs by mapping its latent space to only the collision-free areas of the robot joint space. Our method simply provides this collision-free latent space after which any planner, using any optim
Externí odkaz:
http://arxiv.org/abs/2202.07203
We achieved contact-rich flexible object manipulation, which was difficult to control with vision alone. In the unzipping task we chose as a validation task, the gripper grasps the puller, which hides the bag state such as the direction and amount of
Externí odkaz:
http://arxiv.org/abs/2112.06442