ARMP: Autoregressive Motion Planning for Quadruped Locomotion and Navigation in Complex Indoor Environments
Jeonghwan Kim,Tianyu Li,Sehoon Ha,Jeonghwan Kim,Tianyu Li,Sehoon Ha
Generating natural and physically feasible motions for legged robots has been a challenging problem due to its complex dynamics. In this work, we introduce a novel learning-based framework of autoregressive motion planner (ARMP) for quadruped locomotion and navigation. Our method can generate motion plans with an arbitrary length in an autore-gressive fashion, unlike most offline trajectory optimi...
