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pro vyhledávání: '"Karumanchi, Sambhu H."'
We introduce $\mathcal{L}_1$-MBRL, a control-theoretic augmentation scheme for Model-Based Reinforcement Learning (MBRL) algorithms. Unlike model-free approaches, MBRL algorithms learn a model of the transition function using data and use it to desig
Externí odkaz:
http://arxiv.org/abs/2403.14860
Visual-inertial SLAM is essential for robot navigation in GPS-denied environments, e.g. indoor, underground. Conventionally, the performance of visual-inertial SLAM is evaluated with open-loop analysis, with a focus on the drift level of SLAM systems
Externí odkaz:
http://arxiv.org/abs/2003.01317
Publikováno v:
In IFAC PapersOnLine 2023 56(2):11235-11241
