March 8, 2024, 5:42 a.m. | Guangyu Zhao, Tianyue Wu, Yeke Chen, Fei Gao

cs.LG updates on arXiv.org arxiv.org

arXiv:2403.04586v1 Announce Type: cross
Abstract: Animals learn to adapt agility of their movements to their capabilities and the environment they operate in. Mobile robots should also demonstrate this ability to combine agility and safety. The aim of this work is to endow flight vehicles with the ability of agility adaptation in prior unknown and partially observable cluttered environments. We propose a hierarchical learning and planning framework where we utilize both trial and error to comprehensively learn an agility policy with …

abstract adapt agility aim animals arxiv capabilities cs.lg cs.ro environment learn mobile movements prior robots safety the environment type vehicles work

Data Architect

@ University of Texas at Austin | Austin, TX

Data ETL Engineer

@ University of Texas at Austin | Austin, TX

Lead GNSS Data Scientist

@ Lurra Systems | Melbourne

Senior Machine Learning Engineer (MLOps)

@ Promaton | Remote, Europe

Software Engineer, Machine Learning (Tel Aviv)

@ Meta | Tel Aviv, Israel

Senior Data Scientist- Digital Government

@ Oracle | CASABLANCA, Morocco