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Dyna-LfLH: Learning Agile Navigation in Dynamic Environments from Learned Hallucination
March 27, 2024, 4:42 a.m. | Saad Abdul Ghani, Zizhao Wang, Peter Stone, Xuesu Xiao
cs.LG updates on arXiv.org arxiv.org
Abstract: This paper presents a self-supervised learning method to safely learn a motion planner for ground robots to navigate environments with dense and dynamic obstacles. When facing highly-cluttered, fast-moving, hard-to-predict obstacles, classical motion planners may not be able to keep up with limited onboard computation. For learning-based planners, high-quality demonstrations are difficult to acquire for imitation learning while reinforcement learning becomes inefficient due to the high probability of collision during exploration. To safely and efficiently provide …
abstract agile arxiv computation cs.lg cs.ro dynamic environments hallucination learn moving navigation obstacles paper robots self-supervised learning supervised learning type
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