# Learning to walk in confined spaces using 3D representation

arXiv: https://arxiv.org/abs/2403.00187 · HTML: https://mlsanity.com/paper/2403.00187/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Feb 2024.

## Built on by (most cited first, 22 of 22)

- [Deep Reinforcement Learning for Robotics: A Survey of Real-World Successes](https://mlsanity.com/paper/2408.03539/) (2024)
- [Learning Visual Quadrupedal Loco-Manipulation from Demonstrations](https://mlsanity.com/paper/2403.20328/) (2024)
- [RobotKeyframing: Learning Locomotion with High-Level Objectives via Mixture of Dense and Sparse Rewards](https://mlsanity.com/paper/2407.11562/) (2024)
- [SoloParkour: Constrained Reinforcement Learning for Visual Locomotion from Privileged Experience](https://mlsanity.com/paper/2409.13678/) (2024)
- [Identifying Terrain Physical Parameters from Vision -- Towards Physical-Parameter-Aware Locomotion and Navigation](https://mlsanity.com/paper/2408.16567/) (2024)
- [Embrace Collisions: Humanoid Shadowing for Deployable Contact-Agnostics Motions](https://mlsanity.com/paper/2502.01465/) (2025)
- [Flying on Point Clouds with Reinforcement Learning](https://mlsanity.com/paper/2503.00496/) (2025)
- [Whole-body End-Effector Pose Tracking](https://mlsanity.com/paper/2409.16048/) (2024)
- [Adaptive Diffusion Terrain Generator for Autonomous Uneven Terrain Navigation](https://mlsanity.com/paper/2410.10766/) (2024)
- [Dynamic object goal pushing with mobile manipulators through model-free constrained reinforcement learning](https://mlsanity.com/paper/2502.01546/) (2025)
- [Forging Spatial Intelligence: A Roadmap of Multi-Modal Data Pre-Training for Autonomous Systems](https://mlsanity.com/paper/2512.24385/) (2025)
- [Learning Human-Like Badminton Skills for Humanoid Robots](https://mlsanity.com/paper/2602.08370/) (2026)
- [QuietPaw: Learning Quadrupedal Locomotion with Versatile Noise Preference Alignment](https://mlsanity.com/paper/2503.05035/) (2025)
- [Path Planning and Reinforcement Learning-Driven Control of On-Orbit Free-Flying Multi-Arm Robots](https://mlsanity.com/paper/2603.23182/) (2026)
- [Agile perceptive multi-skill locomotion for quadrupedal robots in the wild](https://mlsanity.com/paper/2607.13579/) (2026)
- [Parental Guidance: Efficient Lifelong Learning through Evolutionary Distillation](https://mlsanity.com/paper/2503.18531/) (2025)
- [ADEPT: Adaptive Diffusion Environment for Policy Transfer Sim-to-Real](https://mlsanity.com/paper/2506.01759/) (2025)
- [PPL: Point Cloud Supervised Proprioceptive Locomotion Reinforcement Learning for Legged Robots in Crawl Spaces](https://mlsanity.com/paper/2508.09950/) (2025)
- [Acrobotics: A Generalist Approach to Quadrupedal Robots' Parkour](https://mlsanity.com/paper/2509.02727/) (2025)
- [CMR: Contractive Mapping Embeddings for Robust Humanoid Locomotion on Unstructured Terrains](https://mlsanity.com/paper/2602.03511/) (2026)
- [Robot Squid Game: Quadrupedal Locomotion for Traversing Narrow Tunnels](https://mlsanity.com/paper/2605.13665/) (2026)
- [E2DRL: End-to-End Differentiable Reinforcement Learning for 3D Obstacle Avoidance in Quadruped Robots](https://mlsanity.com/paper/2610.19627/) (2026)

## Abstract (arXiv)

Legged robots have the potential to traverse complex terrain and access confined spaces beyond the reach of traditional platforms thanks to their ability to carefully select footholds and flexibly adapt their body posture while walking. However, robust deployment in real-world applications is still an open challenge. In this paper, we present a method for legged locomotion control using reinforcement learning and 3D volumetric representations to enable robust and versatile locomotion in confined and unstructured environments. By employing a two-layer hierarchical policy structure, we exploit the capabilities of a highly robust low-level policy to follow 6D commands and a high-level policy to enable three-dimensional spatial awareness for navigating under overhanging obstacles. Our study includes the development of a procedural terrain generator to create diverse training environments. We present a series of experimental evaluations in both simulation and real-world settings, demonstrating the effectiveness of our approach in controlling a quadruped robot in confined, rough terrain. By achieving this, our work extends the applicability of legged robots to a broader range of scenarios.