mlsanityData to 14 Oct 2026

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

arXiv 2508.14098 ↗First posted 16 Aug 2025cs.RO cs.AIcited by 0

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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.

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.

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19 of 30 references.

References · 19

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2022Robot Operating System 2: Design, Architecture, and Uses In The Wildcited by 199
  3. 2023Real-World Humanoid Locomotion with Reinforcement Learningcited by 187
  4. 2023Universal Humanoid Motion Representations for Physics-Based Controlcited by 116
  5. 2024Reinforcement Learning for Versatile, Dynamic, and Robust Bipedal Locomotion Controlcited by 99
  6. 2023CALM: Conditional Adversarial Latent Models for Directable Virtual Characterscited by 94
  7. 2021Blind Bipedal Stair Traversal via Sim-to-Real Reinforcement Learningcited by 92
  8. 2022Motion Policy Networkscited by 55
  9. 2023C·ASE: Learning Conditional Adversarial Skill Embeddings for Physics-based Characterscited by 55
  10. 2021Transferring Dexterous Manipulation from GPU Simulation to a Remote Real-World TriFingercited by 38
  11. 2020Learning Memory-Based Control for Human-Scale Bipedal Locomotioncited by 28
  12. 2024Symmetry Considerations for Learning Task Symmetric Robot Policiescited by 28
7 more
  1. 2024Revisiting Reward Design and Evaluation for Robust Humanoid Standing and Walkingcited by 21
  2. 2024Learning Multi-Modal Whole-Body Control for Real-World Humanoid Robotscited by 16
  3. 2025StyleLoco: Generative Adversarial Distillation for Natural Humanoid Robot Locomotioncited by 8
  4. 2025Natural Humanoid Robot Locomotion with Generative Motion Priorcited by 7
  5. 2024Whole-body End-Effector Pose Trackingcited by 6
  6. 2024Achieving Stable High-Speed Locomotion for Humanoid Robots with Deep Reinforcement Learningcited by 6
  7. 2025Generating Physically Realistic and Directable Human Motions from Multi-Modal Inputscited by 6

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  2. 2025BeamDojo: Learning Agile Humanoid Locomotion on Sparse Footholds
  3. 2026Walk the PLANC: Physics-Guided RL for Agile Humanoid Locomotion on Constrained Footholds
  4. 2026Passive-Dynamic-Walking-Inspired Dynamics Guidance for Energy-Efficient Humanoid Locomotion ↗
  5. 2025Physics-Guided Residual Reinforcement Learning for Humanoid Narrow-Path Traversal
  6. 2026HuMBLE: Human Motion-Driven Behavior Learning for Embodied Locomotion ↗
  7. 2026HuGo: LLMs as Whole-Body Policy Code Designers for Humanoid Loco-Manipulation
  8. 2026Locomotion Beyond Feet