# No More Marching: Learning Humanoid Locomotion for Short-Range SE(2) Targets

arXiv: https://arxiv.org/abs/2508.14098 · HTML: https://mlsanity.com/paper/2508.14098/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 30 references that are in the map.
- First posted on arXiv 16 Aug 2025.

## Builds on (most cited first, 19 of 19)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Robot Operating System 2: Design, Architecture, and Uses In The Wild](https://mlsanity.com/paper/2211.07752/) (2022)
- [Real-World Humanoid Locomotion with Reinforcement Learning](https://mlsanity.com/paper/2303.03381/) (2023)
- [Universal Humanoid Motion Representations for Physics-Based Control](https://mlsanity.com/paper/2310.04582/) (2023)
- [Reinforcement Learning for Versatile, Dynamic, and Robust Bipedal Locomotion Control](https://mlsanity.com/paper/2401.16889/) (2024)
- [CALM: Conditional Adversarial Latent Models for Directable Virtual Characters](https://mlsanity.com/paper/2305.02195/) (2023)
- [Blind Bipedal Stair Traversal via Sim-to-Real Reinforcement Learning](https://mlsanity.com/paper/2105.08328/) (2021)
- [Motion Policy Networks](https://mlsanity.com/paper/2210.12209/) (2022)
- [C·ASE: Learning Conditional Adversarial Skill Embeddings for Physics-based Characters](https://mlsanity.com/paper/2309.11351/) (2023)
- [Transferring Dexterous Manipulation from GPU Simulation to a Remote Real-World TriFinger](https://mlsanity.com/paper/2108.09779/) (2021)
- [Learning Memory-Based Control for Human-Scale Bipedal Locomotion](https://mlsanity.com/paper/2006.02402/) (2020)
- [Symmetry Considerations for Learning Task Symmetric Robot Policies](https://mlsanity.com/paper/2403.04359/) (2024)
- [Revisiting Reward Design and Evaluation for Robust Humanoid Standing and Walking](https://mlsanity.com/paper/2404.19173/) (2024)
- [Learning Multi-Modal Whole-Body Control for Real-World Humanoid Robots](https://mlsanity.com/paper/2408.07295/) (2024)
- [StyleLoco: Generative Adversarial Distillation for Natural Humanoid Robot Locomotion](https://mlsanity.com/paper/2503.15082/) (2025)
- [Natural Humanoid Robot Locomotion with Generative Motion Prior](https://mlsanity.com/paper/2503.09015/) (2025)
- [Whole-body End-Effector Pose Tracking](https://mlsanity.com/paper/2409.16048/) (2024)
- [Achieving Stable High-Speed Locomotion for Humanoid Robots with Deep Reinforcement Learning](https://mlsanity.com/paper/2409.16611/) (2024)
- [Generating Physically Realistic and Directable Human Motions from Multi-Modal Inputs](https://mlsanity.com/paper/2502.05641/) (2025)

## Abstract (arXiv)

Humanoids operating in real-world workspaces must frequently execute task-driven, short-range movements to SE(2) target poses. To be practical, these transitions must be fast, robust, and energy efficient. While learning-based locomotion has made significant progress, most existing methods optimize for velocity-tracking rather than direct pose reaching, resulting in inefficient, marching-style behavior when applied to short-range tasks. In this work, we develop a reinforcement learning approach that directly optimizes humanoid locomotion for SE(2) targets. Central to this approach is a new constellation-based reward function that encourages natural and efficient target-oriented movement. To evaluate performance, we introduce a benchmarking framework that measures energy consumption, time-to-target, and footstep count on a distribution of SE(2) goals. Our results show that the proposed approach consistently outperforms standard methods and enables successful transfer from simulation to hardware, highlighting the importance of targeted reward design for practical short-range humanoid locomotion.