Zobrazeno 1 - 10
of 329
pro vyhledávání: '"Miura, Jun"'
We demonstrate experimental results with LLMs that address robotics task planning problems. Recently, LLMs have been applied in robotics task planning, particularly using a code generation approach that converts complex high-level instructions into m
Externí odkaz:
http://arxiv.org/abs/2403.13801
Autor:
Natan, Oskar, Miura, Jun
We present DeepIPCv2, an autonomous driving model that perceives the environment using a LiDAR sensor for more robust drivability, especially when driving under poor illumination conditions where everything is not clearly visible. DeepIPCv2 takes a s
Externí odkaz:
http://arxiv.org/abs/2307.06647
This paper describes a method of domain adaptive training for semantic segmentation using multiple source datasets that are not necessarily relevant to the target dataset. We propose a soft pseudo-label generation method by integrating predicted obje
Externí odkaz:
http://arxiv.org/abs/2303.00979
Publikováno v:
Respirology Case Reports. Aug2024, Vol. 12 Issue 8, p1-4. 4p.
This paper describes a method of online refinement of a scene recognition model for robot navigation considering traversable plants, flexible plant parts which a robot can push aside while moving. In scene recognition systems that consider traversabl
Externí odkaz:
http://arxiv.org/abs/2208.06636
Autor:
Natan, Oskar, Miura, Jun
In this work, we introduce DeepIPC, a novel end-to-end model tailored for autonomous driving, which seamlessly integrates perception and control tasks. Unlike traditional models that handle these tasks separately, DeepIPC innovatively combines a perc
Externí odkaz:
http://arxiv.org/abs/2207.09934
Autor:
Natan, Oskar, Miura, Jun
Focusing on the task of point-to-point navigation for an autonomous driving vehicle, we propose a novel deep learning model trained with end-to-end and multi-task learning manners to perform both perception and control tasks simultaneously. The model
Externí odkaz:
http://arxiv.org/abs/2204.05513
This paper describes a method of estimating the traversability of plant parts covering a path and navigating through them for mobile robots operating in plant-rich environments. Conventional mobile robots rely on scene recognition methods that consid
Externí odkaz:
http://arxiv.org/abs/2108.00759
Autonomous driving systems need to handle complex scenarios such as lane following, avoiding collisions, taking turns, and responding to traffic signals. In recent years, approaches based on end-to-end behavioral cloning have demonstrated remarkable
Externí odkaz:
http://arxiv.org/abs/2104.10753
Publikováno v:
Advanced Robotics, 36:19, 1011-1029 (2022)
This paper describes a novel method of training a semantic segmentation model for scene recognition of agricultural mobile robots exploiting publicly available datasets of outdoor scenes that are different from the target greenhouse environments. Sem
Externí odkaz:
http://arxiv.org/abs/2102.06386