# HWC-Loco: A Hierarchical Whole-Body Control Approach to Robust Humanoid Locomotion

arXiv: https://arxiv.org/abs/2503.00923 · HTML: https://mlsanity.com/paper/2503.00923/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Mar 2025.

## Built on by (most cited first, 3 of 3)

- [PhysHSI: Towards a Real-World Generalizable and Natural Humanoid-Scene Interaction System](https://mlsanity.com/paper/2510.11072/) (2025)
- [MoRE: Mixture of Residual Experts for Humanoid Lifelike Gaits Learning on Complex Terrains](https://mlsanity.com/paper/2506.08840/) (2025)
- [Humanoid Goalkeeper: Learning from Position Conditioned Task-Motion Constraints](https://mlsanity.com/paper/2510.18002/) (2025)

## Abstract (arXiv)

Humanoid robots, capable of assuming human roles in various workplaces, have become essential to embodied intelligence. However, as robots with complex physical structures, learning a control model that can operate robustly across diverse environments remains inherently challenging, particularly under the discrepancies between training and deployment environments. In this study, we propose HWC-Loco, a robust whole-body control algorithm tailored for humanoid locomotion tasks. By reformulating policy learning as a robust optimization problem, HWC-Loco explicitly learns to recover from safety-critical scenarios. While prioritizing safety guarantees, overly conservative behavior can compromise the robot's ability to complete the given tasks. To tackle this challenge, HWC-Loco leverages a hierarchical policy for robust control. This policy can dynamically resolve the trade-off between goal-tracking and safety recovery, guided by human behavior norms and dynamic constraints. To evaluate the performance of HWC-Loco, we conduct extensive comparisons against state-of-the-art humanoid control models, demonstrating HWC-Loco's superior performance across diverse terrains, robot structures, and locomotion tasks under both simulated and real-world environments.