# Robot Squid Game: Quadrupedal Locomotion for Traversing Narrow Tunnels

arXiv: https://arxiv.org/abs/2605.13665 · HTML: https://mlsanity.com/paper/2605.13665/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for robot locomotion and manipulation, one of 15 directions in Deep reinforcement learning methods.
- Builds on 21 of its 27 references that are in the map.
- First posted on arXiv 13 May 2026.

## Placed in

- Deep reinforcement learning methods / [RL for robot locomotion and manipulation](https://mlsanity.com/deeprl/rl-for-robot-locomotion-and-manipulation/)

## Builds on (most cited first, 21 of 21)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning](https://mlsanity.com/paper/2108.10470/) (2021)
- [Learning Quadrupedal Locomotion over Challenging Terrain](https://mlsanity.com/paper/2010.11251/) (2020)
- [RMA: Rapid Motor Adaptation for Legged Robots](https://mlsanity.com/paper/2107.04034/) (2021)
- [Learning agile and dynamic motor skills for legged robots](https://mlsanity.com/paper/1901.08652/) (2019)
- [Policy Distillation](https://mlsanity.com/paper/1511.06295/) (2015)
- [Learning robust perceptive locomotion for quadrupedal robots in the wild](https://mlsanity.com/paper/2201.08117/) (2022)
- [Legged Locomotion in Challenging Terrains using Egocentric Vision](https://mlsanity.com/paper/2211.07638/) (2022)
- [Agile But Safe: Learning Collision-Free High-Speed Legged Locomotion](https://mlsanity.com/paper/2401.17583/) (2024)
- [Learning Vision-Based Bipedal Locomotion for Challenging Terrain](https://mlsanity.com/paper/2309.14594/) (2023)
- [Barkour: Benchmarking Animal-level Agility with Quadruped Robots](https://mlsanity.com/paper/2305.14654/) (2023)
- [Learning to walk in confined spaces using 3D representation](https://mlsanity.com/paper/2403.00187/) (2024)
- [Learning to Navigate Sidewalks in Outdoor Environments](https://mlsanity.com/paper/2109.05603/) (2021)
- [Learning Dynamic Bipedal Walking Across Stepping Stones](https://mlsanity.com/paper/2205.01807/) (2022)
- [Dexterous Legged Locomotion in Confined 3D Spaces with Reinforcement Learning](https://mlsanity.com/paper/2403.03848/) (2024)
- [ArtPlanner: Robust Legged Robot Navigation in the Field](https://mlsanity.com/paper/2303.01420/) (2023)
- [Walking with Terrain Reconstruction: Learning to Traverse Risky Sparse Footholds](https://mlsanity.com/paper/2409.15692/) (2024)
- [Terrain Recognition and Contact Force Estimation through a Sensorized Paw for Legged Robots](https://mlsanity.com/paper/2311.03855/) (2023)
- [Walking Posture Adaptation for Legged Robot Navigation in Confined Spaces](https://mlsanity.com/paper/1901.10863/) (2019)
- [Terrain-perception-free Quadrupedal Spinning Locomotion on Versatile Terrains: Modeling, Analysis, and Experimental Validation](https://mlsanity.com/paper/2107.12479/) (2021)

## Abstract (arXiv)

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