Terrain-Aware Quadrupedal Locomotion via Reinforcement Learning

TL;DR

This paper introduces a reinforcement learning-based method for quadruped robots to adaptively traverse challenging terrains by integrating perception with a parameterized trajectory generator, enhancing safety and efficiency.

Contribution

It presents a novel DNN policy that modifies trajectory parameters based on terrain perception, enabling safer and more energy-efficient quadruped locomotion over rough terrains.

Findings

Successfully traverses stairs, stepping stones, and poles in simulation.

Real robot experiments demonstrate crossing gaps over 25.5cm.

Policy adapts to various terrains in any direction.

Abstract

In nature, legged animals have developed the ability to adapt to challenging terrains through perception, allowing them to plan safe body and foot trajectories in advance, which leads to safe and energy-efficient locomotion. Inspired by this observation, we present a novel approach to train a Deep Neural Network (DNN) policy that integrates proprioceptive and exteroceptive states with a parameterized trajectory generator for quadruped robots to traverse rough terrains. Our key idea is to use a DNN policy that can modify the parameters of the trajectory generator, such as foot height and frequency, to adapt to different terrains. To encourage the robot to step on safe regions and save energy consumption, we propose foot terrain reward and lifting foot height reward, respectively. By incorporating these rewards, our method can learn a safer and more efficient terrain-aware locomotion policy that can move a quadruped robot flexibly in any direction. To evaluate the effectiveness of our approach, we conduct simulation experiments on challenging terrains, including stairs, stepping stones, and poles. The simulation results demonstrate that our approach can successfully direct the robot to traverse such tough terrains in any direction. Furthermore, we validate our method on a real legged robot, which learns to traverse stepping stones with gaps over 25.5cm.