Robot Squid Game: Quadrupedal Locomotion for Traversing Narrow Tunnels

2026-05-13

AI Summary

This work addresses the challenge of autonomous navigation for quadrupedal robots in confined three-dimensional environments—such as tunnels and caves—where rigid gaits and limited environmental adaptability hinder performance. The authors propose a reinforcement learning framework that integrates procedural environment generation with policy distillation, leveraging a teacher–student paradigm to transfer knowledge from multiple expert policies into a unified student policy. By decomposing the complex traversal task into learnable subtasks, the approach circumvents the difficulties of reward engineering inherent in end-to-end training. The resulting policy demonstrates significantly enhanced generalization, achieving robust and stable navigation through diverse constrained tunnel structures in both simulation and real-world experiments, outperforming existing methods.

Abstract

Quadruped robots demonstrate exceptional potential for navigating complex terrain in critical applications such as search and rescue missions and infrastructure inspection However autonomous traversal of confined 3D environments including tunnels caves and collapsed structures remains a significant challenge Existing methods often struggle with rigid gait patterns limited adaptability to diverse geometries and reliance on oversimplified environmental assumptions This paper introduces a Reinforcement Learning RL framework that combines procedural environment generation with policy distillation to enable robust locomotion across various tunnel configurations Our approach leverages a teacher student training paradigm where specialized expert policies trained on procedurally generated tunnel geometries transfer their knowledge to a unified student policy This strategy eliminates the need for complex reward shaping in end-to-end RL training simplifying the process by breaking down complicated tasks into smaller more manageable components that are easier for the robot to learn By synthesizing diverse tunnel structures during training and distilling navigation strategies into a generalizable policy our method achieves consistent traversal across complex spatial constraints where conventional approaches fail We demonstrate through both simulation and real world experiments that our method enables quadruped robots to successfully traverse challenging confined tunnel environments

Problem

• quadrupedal locomotion
• confined environments
• narrow tunnels
• autonomous traversal
• complex terrain

Innovation

• Reinforcement Learning
• Policy Distillation
• Procedural Environment Generation
• Quadrupedal Locomotion
• Confined Space Navigation