Fast Footstep Planning on Uneven Terrain Using Deep Sequential Models

TL;DR

This paper introduces an LSTM-based footstep planning method for legged robots that efficiently considers terrain and dynamics, enabling fast and reliable navigation over uneven terrain.

Contribution

The work presents a novel deep sequential model that learns to predict footstep locations considering terrain and robot dynamics, significantly improving planning speed and accuracy.

Findings

LSTM-based planner operates in linear time.

Effective in diverse uneven terrains.

Speeds up sampling-based planning modules.

Abstract

One of the fundamental challenges in realizing the potential of legged robots is generating plans to traverse challenging terrains. Control actions must be carefully selected so the robot will not crash or slip. The high dimensionality of the joint space makes directly planning low-level actions from onboard perception difficult, and control stacks that do not consider the low-level mechanisms of the robot in planning are ill-suited to handle fine-grained obstacles. One method for dealing with this is selecting footstep locations based on terrain characteristics. However, incorporating robot dynamics into footstep planning requires significant computation, much more than in the quasi-static case. In this work, we present an LSTM-based planning framework that learns probability distributions over likely footstep locations using both terrain lookahead and the robot's dynamics, and leverages the LSTM's sequential nature to find footsteps in linear time. Our framework can also be used as a module to speed up sampling-based planners. We validate our approach on a simulated one-legged hopper over a variety of uneven terrains.