  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 autoregressive fashion, unlike most offline
trajectory optimization algorithms for a fixed trajectory length. To this end,
we first construct the motion library by solving a dense set of trajectory
optimization problems for diverse scenarios and parameter settings. Then we
learn the motion manifold from the dataset in a supervised learning fashion. We
show that the proposed ARMP can generate physically plausible motions for
various tasks and situations. We also showcase that our method can be
successfully integrated with the recent robot navigation frameworks as a
low-level controller and unleash the full capability of legged robots for
complex indoor navigation.
