# Attention-Based Map Encoding for Learning Generalized Legged Locomotion

arXiv: https://arxiv.org/abs/2506.09588 · HTML: https://mlsanity.com/paper/2506.09588/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jun 2025.

## Built on by (most cited first, 30 of 40)

- [BeyondMimic: From Motion Tracking to Versatile Humanoid Control via Guided Diffusion](https://mlsanity.com/paper/2508.08241/) (2025)
- [OmniRetarget: Interaction-Preserving Data Generation for Humanoid Whole-Body Loco-Manipulation and Scene Interaction](https://mlsanity.com/paper/2509.26633/) (2025)
- [RSL-RL: A Learning Library for Robotics Research](https://mlsanity.com/paper/2509.10771/) (2025)
- [Perceptive Humanoid Parkour: Chaining Dynamic Human Skills via Motion Matching](https://mlsanity.com/paper/2602.15827/) (2026)
- [DreamControl: Human-Inspired Whole-Body Humanoid Control for Scene Interaction via Guided Diffusion](https://mlsanity.com/paper/2509.14353/) (2025)
- [Hiking in the Wild: A Scalable Perceptive Parkour Framework for Humanoids](https://mlsanity.com/paper/2601.07718/) (2026)
- [Towards Adaptable Humanoid Control via Adaptive Motion Tracking](https://mlsanity.com/paper/2510.14454/) (2025)
- [Olaf: Bringing an Animated Character to Life in the Physical World](https://mlsanity.com/paper/2512.16705/) (2025)
- [Humanoid Occupancy: Enabling A Generalized Multimodal Occupancy Perception System on Humanoid Robots](https://mlsanity.com/paper/2507.20217/) (2025)
- [PolicyFlow: Policy Optimization with Continuous Normalizing Flow in Reinforcement Learning](https://mlsanity.com/paper/2602.01156/) (2026)
- [LUCID: Learning Embodiment-Agnostic Intent Models from Unstructured Human Videos for Scalable Dexterous Robot Skill Acquisition](https://mlsanity.com/paper/2606.11628/) (2026)
- [TTT-Parkour: Rapid Test-Time Training for Perceptive Robot Parkour](https://mlsanity.com/paper/2602.02331/) (2026)
- [Contrastive Representation Learning for Robust Sim-to-Real Transfer of Adaptive Humanoid Locomotion](https://mlsanity.com/paper/2509.12858/) (2025)
- [RobotDancing: Residual-Action Reinforcement Learning Enables Robust Long-Horizon Humanoid Motion Tracking](https://mlsanity.com/paper/2509.20717/) (2025)
- [DeFM: Learning Foundation Representations from Depth for Robotics](https://mlsanity.com/paper/2601.18923/) (2026)
- [Closing the Reality Gap: Zero-Shot Sim-to-Real Deployment for Dexterous Force-Based Grasping and Manipulation](https://mlsanity.com/paper/2601.02778/) (2026)
- [Flip Stunts on Bicycle Robots using Iterative Motion Imitation](https://mlsanity.com/paper/2603.27944/) (2026)
- [Physics-Guided Residual Reinforcement Learning for Humanoid Narrow-Path Traversal](https://mlsanity.com/paper/2508.20661/) (2025)
- [Architecture Is All You Need: Diversity-Enabled Sweet Spots for Robust Humanoid Locomotion](https://mlsanity.com/paper/2510.14947/) (2025)
- [HAFO: A Force-Adaptive Control Framework for Humanoid Robots in Intense Interaction Environments](https://mlsanity.com/paper/2511.20275/) (2025)
- [PUMA: Perception-driven Unified Foothold Prior for Mobility Augmented Quadruped Parkour](https://mlsanity.com/paper/2601.15995/) (2026)
- [APEX: Learning Adaptive High-Platform Traversal for Humanoid Robots](https://mlsanity.com/paper/2602.11143/) (2026)
- [Generalizing from References using a Multi-Task Reference and Goal-Driven RL Framework](https://mlsanity.com/paper/2602.20375/) (2026)
- [LineRides: Line-Guided Reinforcement Learning for Bicycle Robot Stunts](https://mlsanity.com/paper/2605.05110/) (2026)
- [Global-Local Attention Decomposition for Terrain Encoding in Humanoid Perceptive Locomotion](https://mlsanity.com/paper/2606.00637/) (2026)
- [Mind Your Steps: A General Learning Framework for Accurate Humanoid Foothold Tracking](https://mlsanity.com/paper/2606.08253/) (2026)
- [Trajectory-Level Automatic Curriculum Learning for Legged Locomotion on Unstructured Terrain](https://mlsanity.com/paper/2608.16164/) (2026)
- [BRIDGE: An Open-Source Humanoid Platform via Morphology-Control Co-Design for Physical AI](https://mlsanity.com/paper/2609.03497/) (2026)
- [GeCCo -- a Generalist Contact-Conditioned Policy for Loco-Manipulation Skills on Legged Robots](https://mlsanity.com/paper/2509.17582/) (2025)
- [Kinematics-Aware Multi-Policy Reinforcement Learning for Force-Capable Humanoid Loco-Manipulation](https://mlsanity.com/paper/2511.21169/) (2025)

## Abstract (arXiv)

Dynamic locomotion of legged robots is a critical yet challenging topic in expanding the operational range of mobile robots. It requires precise planning when possible footholds are sparse, robustness against uncertainties and disturbances, and generalizability across diverse terrains. While traditional model-based controllers excel at planning on complex terrains, they struggle with real-world uncertainties. Learning-based controllers offer robustness to such uncertainties but often lack precision on terrains with sparse steppable areas. Hybrid methods achieve enhanced robustness on sparse terrains by combining both methods but are computationally demanding and constrained by the inherent limitations of model-based planners. To achieve generalized legged locomotion on diverse terrains while preserving the robustness of learning-based controllers, this paper proposes to learn an attention-based map encoding conditioned on robot proprioception, which is trained as part of the end-to-end controller using reinforcement learning. We show that the network learns to focus on steppable areas for future footholds when the robot dynamically navigates diverse and challenging terrains. We synthesize behaviors that exhibit robustness against uncertainties while enabling precise and agile traversal of sparse terrains. Additionally, our method offers a way to interpret the topographical perception of a neural network. We have trained two controllers for a 12-DoF quadrupedal robot and a 23-DoF humanoid robot respectively and tested the resulting controllers in the real world under various challenging indoor and outdoor scenarios, including ones unseen during training.