Metadata only
Date
2020Type
- Conference Paper
Abstract
Mobile robots operating in public environments require the ability to navigate among humans and other obstacles in a socially compliant and safe manner. This work presents a combined imitation learning and deep reinforcement learning approach for motion planning in such crowded and cluttered environments. By separately processing information related to static and dynamic objects, we enable our network to learn motion patterns that are tailored to real-world environments. Our model is also designed such that it can handle usual cases in which robots can be equipped with sensor suites that only offer limited field of view. Our model outperforms current state-of-the-art approaches, which is shown in simulated environments containing human-like agents and static obstacles. Additionally, we demonstrate the real-time performance and applicability of our model by successfully navigating a robotic platform through real-world environments. © 2020 IEEE. Show more
Publication status
publishedExternal links
Book title
2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)Pages / Article No.
Publisher
IEEEEvent
Notes
Due to the Coronavirus (COVID-19) the conference was conducted virtually.More
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