LEEPS: Learning End-to-End Legged Perceptive Parkour Skills on Challenging Terrains

Abstract

Empowering legged robots with agile maneuvers is a great challenge. While existing works have proposed diverse control-based and learning-based methods, it remains an open problem to endow robots with animal-like perception and athleticism. Towards this goal, we develop an End-to-End Legged Perceptive Parkour Skill Learning (LEEPS) framework to train quadruped robots to master parkour skills in complex environments. In particular, LEEPS incorporates a vision-based perception module equipped with multi-layered scans, supplying robots with comprehensive, precise, and adaptable information about their surroundings. Leveraging such visual data, a position-based task formulation liberates the robot from velocity constraints and directs it toward the target using innovative reward mechanisms. The resulting controller empowers an affordable quadruped robot to successfully navigate previously challenging and unprecedented obstacles.We evaluate LEEPS on various challenging tasks, which demonstrate its effectiveness, robustness, and generalizability.

Method

Main Results

1. Parkour Performance on Nine Challenging Terrains