ARMP: Autoregressive Motion Planning for Quadruped Locomotion and Navigation in Complex Indoor Environments

TL;DR

This paper introduces ARMP, a novel autoregressive learning-based motion planner for quadruped robots that generates diverse, physically plausible motions for complex indoor navigation, surpassing traditional fixed-length trajectory methods.

Contribution

The paper presents a new autoregressive framework that learns a motion manifold from a dataset, enabling flexible, long-horizon motion planning for quadruped robots in complex environments.

Findings

ARMP generates physically plausible motions for various tasks.

ARMP can be integrated with navigation frameworks as a low-level controller.

The method enhances the capabilities of legged robots in indoor navigation.

Abstract

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.