Apr 15, 2019 · We proposed a new motion planner based on deep reinforcement learning that can arrive at new targets that have not been trained before in the indoor ...
We proposed a new motion planner based on deep reinforcement learning that can arrive at new targets that have not been trained before in the indoor environment ...
The second and third levels require the robot to have the ability of reasoning. In the second level, the robot needs to arrive at unseen targets in the same ...
This work proposed a new motion planner based on deep reinforcement learning that can arrive at new targets that have not been trained before in the indoor ...
We evaluate on real navigation scenarios, explore different localization and point goal calculation methods and report significant gains in performance and ...
Missing: Memorizing | Show results with:Memorizing
The goal of this research is to enable mobile robots to navigate through crowded environments such as indoor shopping malls, airports, or downtown side ...
It's a collection for mapless robot navigation using RGB image as visual input. It contains the test environment and motion planners, aiming at realizing ...
Abstract. This article presents a system in which a mobile robot equipped with a range sensor eЖciently builds polygonal layouts of indoor environments as ...
This study offers new insight on the role of individual visuospatial measures in environment learning (navigation-like) and how they are related.
Learning to Navigate in Indoor Environments: from Memorizing to Reasoning. Liulong Ma, Yanjie Liu, Jiao Chen, and Dong Jin. 2019-04-23. cs.RO. Autonomous ...