Learning Whom to Trust in Navigation: Dynamically Switching Between Classical and Neural Planning
Sombit Dey,Assem Sadek,Gianluca Monaci,Boris Chidlovskii,Christian Wolf,Sombit Dey,Assem Sadek,Gianluca Monaci,Boris Chidlovskii,Christian Wolf
Navigation of terrestrial robots is typically addressed either with localization and mapping (SLAM) followed by classical planning on the dynamically created maps, or by machine learning (ML), often through end-to-end training with reinforcement learning (RL) or imitation learning (IL). Recently, modular designs have achieved promising results, and hybrid algorithms that combine ML with classical ...
