Learning Forward Dynamics Model and Informed Trajectory Sampler for Safe Quadruped Navigation

TL;DR

This paper introduces a novel autonomous quadruped navigation framework combining a learned forward dynamics model, an informed trajectory sampler, and an online model-predictive controller to enhance safety, smoothness, and obstacle avoidance in complex environments.

Contribution

It presents a fully learning-based navigation system with a learned forward dynamics model, an informed trajectory sampler, and an online MPC, improving safety and performance over existing methods.

Findings

The framework enables collision-free navigation in complex environments.

It produces smoother command plans compared to baseline methods.

The system can reactively avoid unexpected obstacles.

Abstract

For autonomous quadruped robot navigation in various complex environments, a typical SOTA system is composed of four main modules -- mapper, global planner, local planner, and command-tracking controller -- in a hierarchical manner. In this paper, we build a robust and safe local planner which is designed to generate a velocity plan to track a coarsely planned path from the global planner. Previous works used waypoint-based methods (e.g. Proportional-Differential control and pure pursuit) which simplify the path tracking problem to local point-goal navigation. However, they suffer from frequent collisions in geometrically complex and narrow environments because of two reasons; the global planner uses a coarse and inaccurate model and the local planner is unable to track the global plan sufficiently well. Currently, deep learning methods are an appealing alternative because they can learn safety and path feasibility from experience more accurately. However, existing deep learning methods are not capable of planning for a long horizon. In this work, we propose a learning-based fully autonomous navigation framework composed of three innovative elements: a learned forward dynamics model (FDM), an online sampling-based model-predictive controller, and an informed trajectory sampler (ITS). Using our framework, a quadruped robot can autonomously navigate in various complex environments without a collision and generate a smoother command plan compared to the baseline method. Furthermore, our method can reactively handle unexpected obstacles on the planned path and avoid them. Project page https://awesomericky.github.io/projects/FDM_ITS_navigation/.