5 citations · 20 across the 10 of their papers we have counts for
10 papers
Object Memory Transformer for Object Goal Navigation
Rui Fukushima, Kei Ota, Asako Kanezaki +2
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-te…
Adaptive Future Frame Prediction with Ensemble Network
Wonjik Kim, Masayuki Tanaka, Masatoshi Okutomi +1
Future frame prediction in videos is a challenging problem because videos include complicated movements and large appearance changes. Learning-based future frame prediction approac…
Deep Reactive Planning in Dynamic Environments
Kei Ota, Devesh K. Jha, Tadashi Onishi +5
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. While goal conditi…
Self-supervised Neural Audio-Visual Sound Source Localization via Probabilistic Spatial Modeling
Yoshiki Masuyama, Yoshiaki Bando, Kohei Yatabe +3
Detecting sound source objects within visual observation is important for autonomous robots to comprehend surrounding environments. Since sounding objects have a large variety with…
3D Object Detection Method Based on YOLO and K-Means for Image and Point Clouds
Xuanyu Yin, Yoko Sasaki, Weimin Wang +1
Lidar based 3D object detection and classification tasks are essential for autonomous driving(AD). A lidar sensor can provide the 3D point cloud data reconstruction of the surround…
YOLO and K-Means Based 3D Object Detection Method on Image and Point Cloud
Xuanyu YIN, Yoko SASAKI, Weimin WANG +1
Lidar based 3D object detection and classification tasks are essential for automated driving(AD). A Lidar sensor can provide the 3D point coud data reconstruction of the surroundin…
Learning-Based Human Segmentation and Velocity Estimation Using Automatic Labeled LiDAR Sequence for Training
Wonjik Kim, Masayuki Tanaka, Masatoshi Okutomi +1
In this paper, we propose an automatic labeled sequential data generation pipeline for human segmentation and velocity estimation with point clouds. Considering the impact of deep…
Efficient Exploration in Constrained Environments with Goal-Oriented Reference Path
Kei Ota, Yoko Sasaki, Devesh K. Jha +2
In this paper, we consider the problem of building learning agents that can efficiently learn to navigate in constrained environments. The main goal is to design agents that can ef…
Deep Bayesian Unsupervised Source Separation Based on a Complex Gaussian Mixture Model
Yoshiaki Bando, Yoko Sasaki, Kazuyoshi Yoshii
This paper presents an unsupervised method that trains neural source separation by using only multichannel mixture signals. Conventional neural separation methods require a lot of…
Automatic Labeled LiDAR Data Generation based on Precise Human Model
Wonjik Kim, Masayuki Tanaka, Masatoshi Okutomi +1
Following improvements in deep neural networks, state-of-the-art networks have been proposed for human recognition using point clouds captured by LiDAR. However, the performance of…