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Learning Real-world Autonomous Navigation by Self-Supervised Environment Synthesis.
Zifan
Xu, Anirudh Nair, Xuesu Xiao, and Peter
Stone.
In IROS Workshop on Photorealistic Image and Environment Synthesis for Robotics (PIES-Rob) , January
2023.
Machine learning approaches have recently enabled autonomous navigation for mobile robots in a data-driven manner. Since most existing learning-based navigation systems are trained with data generated in artificially created training environments, during real-world deployment at scale, it is inevitable that robots will encounter unseen scenarios, which are out of the training distribution and therefore lead to poor real-world performance. On the other hand, directly training in the real world is generally unsafe and inefficient. To address this issue, we introduce Self-supervised Environment Synthesis (SES), in which, after real-world deployment with safety and efficiency requirements, autonomous mobile robots can utilize experience from the real-world deployment, reconstruct navigation scenarios, and synthesize representative training environments in simulation. Training in these synthesized environments leads to improved future performance. in the real world. In our experiments, the effectiveness of SES in synthesizing representative simulation environments and improving real-world navigation performance has been verified by a large-scale deployment in a high-fidelity, realistic simulator
@InProceedings{zifan_xu_iros_2023,
author = {Zifan Xu and Anirudh Nair and Xuesu Xiao and Peter Stone},
title = {Learning Real-world Autonomous Navigation by Self-Supervised Environment Synthesis},
booktitle = {IROS Workshop on Photorealistic Image and Environment Synthesis for Robotics (PIES-Rob) },
year = {2023},
month = {January},
location = {Detroit, Michigan},
abstract = {Machine learning approaches have recently enabled autonomous navigation for mobile robots in a data-driven manner. Since most existing learning-based navigation systems are trained with data generated in artificially created training environments, during real-world deployment at scale, it is inevitable that robots will encounter unseen scenarios, which are out of the training distribution and therefore lead to poor real-world performance. On the other hand, directly training in the real world is generally unsafe and inefficient. To address this issue, we introduce Self-supervised Environment Synthesis (SES), in which, after real-world deployment with safety and efficiency requirements, autonomous mobile robots can utilize experience from the real-world deployment, reconstruct navigation scenarios, and synthesize representative training environments in simulation. Training in these synthesized environments leads to improved future performance. in the real world. In our experiments, the effectiveness of SES in synthesizing representative simulation environments and improving real-world navigation performance has been verified by a large-scale deployment in a high-fidelity, realistic simulator},
}
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