no code implementations • 24 Mar 2022 • Rui Fukushima, Kei Ota, Asako Kanezaki, Yoko SASAKI, Yusuke Yoshiyasu
This paper presents a reinforcement learning method for object goal navigation (ObjNav) where an agent navigates in 3D indoor environments to reach a target object based on long-term observations of objects and scenes.
no code implementations • 13 Nov 2020 • Wonjik Kim, Masayuki Tanaka, Masatoshi Okutomi, Yoko SASAKI
A common limitation of the existing learning-based approaches is a mismatch of training data and test data.
no code implementations • 31 Oct 2020 • Kei Ota, Devesh K. Jha, Tadashi Onishi, Asako Kanezaki, Yusuke Yoshiyasu, Yoko SASAKI, Toshisada Mariyama, Daniel Nikovski
The main novelty of the proposed approach is that it allows a robot to learn an end-to-end policy which can adapt to changes in the environment during execution.
no code implementations • 28 Jul 2020 • Yoshiki Masuyama, Yoshiaki Bando, Kohei Yatabe, Yoko Sasaki, Masaki Onishi, Yasuhiro Oikawa
By incorporating with the spatial information in multichannel audio signals, our method trains deep neural networks (DNNs) to distinguish multiple sound source objects.
no code implementations • 21 Apr 2020 • Xuanyu YIN, Yoko SASAKI, Weimin WANG, Kentaro SHIMIZU
In our research, camera can capture the image to make the Real-time 2D object detection by using YOLO, we transfer the bounding box to node whose function is making 3d object detection on point cloud data from Lidar.
no code implementations • 21 Apr 2020 • Xuanyu YIN, Yoko SASAKI, Weimin WANG, Kentaro SHIMIZU
In our research, Camera can capture the image to make the Real-time 2D Object Detection by using YOLO, I transfer the bounding box to node whose function is making 3d object detection on point cloud data from Lidar.
no code implementations • 11 Mar 2020 • Wonjik Kim, Masayuki Tanaka, Masatoshi Okutomi, Yoko SASAKI
In this paper, we propose an automatic labeled sequential data generation pipeline for human segmentation and velocity estimation with point clouds.
no code implementations • 3 Mar 2020 • Kei Ota, Yoko SASAKI, Devesh K. Jha, Yusuke Yoshiyasu, Asako Kanezaki
Specifically, we train a deep convolutional network that can predict collision-free paths based on a map of the environment-- this is then used by a reinforcement learning algorithm to learn to closely follow the path.
no code implementations • 29 Aug 2019 • Yoshiaki Bando, Yoko SASAKI, Kazuyoshi Yoshii
This paper presents an unsupervised method that trains neural source separation by using only multichannel mixture signals.
1 code implementation • 14 Feb 2019 • Wonjik Kim, Masayuki Tanaka, Masatoshi Okutomi, Yoko SASAKI
We present 500k+ data generated by the proposed pipeline.