mlsanityData to 14 Oct 2026

Learning to Walk in Costume: Adversarial Motion Priors for Aesthetically Constrained Humanoids

arXiv 2509.05581 ↗First posted 6 Sep 2025cs.RO cs.AI cs.SYcited by 1

On the map: Deep reinforcement learning methods → RL for robot locomotion and manipulation

Abstract (arXiv)

We present a Reinforcement Learning (RL)-based locomotion system for Cosmo, a custom-built humanoid robot designed for entertainment applications. Unlike traditional humanoids, entertainment robots present unique challenges due to aesthetic-driven design choices. Cosmo embodies these with a disproportionately large head (16% of total mass), limited sensing, and protective shells that considerably restrict movement. To address these challenges, we apply Adversarial Motion Priors (AMP) to enable the robot to learn natural-looking movements while maintaining physical stability. We develop tailored domain randomization techniques and specialized reward structures to ensure safe sim-to-real, protecting valuable hardware components during deployment. Our experiments demonstrate that AMP generates stable standing and walking behaviors despite Cosmo's extreme mass distribution and movement constraints. These results establish a promising direction for robots that balance aesthetic appeal with functional performance, suggesting that learning-based methods can effectively adapt to aesthetic-driven design constraints.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in RL for robot locomotion and manipulation, one of 15 directions in Deep reinforcement learning methods. Builds on 11 of its 24 references that are in the map. First posted on arXiv 6 Sep 2025.

Builds on

11 of 24 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024Reinforcement Learning for Versatile, Dynamic, and Robust Bipedal Locomotion Control30 Jan

Other references · 10

  1. 2021Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learningcited by 998
  2. 2021Learning to Walk in Minutes Using Massively Parallel Deep Reinforcement Learningcited by 512
  3. 2023Real-World Humanoid Locomotion with Reinforcement Learningcited by 187
  4. 2024Expressive Whole-Body Control for Humanoid Robotscited by 139
  5. 2021Reinforcement Learning for Robust Parameterized Locomotion Control of Bipedal Robotscited by 116
  6. 2024Humanoid Parkour Learningcited by 96
  7. 2022Adversarial Motion Priors Make Good Substitutes for Complex Reward Functionscited by 89
  8. 2024WoCoCo: Learning Whole-Body Humanoid Control with Sequential Contactscited by 61
  9. 2025Humanoid Locomotion and Manipulation: Current Progress and Challenges in Control, Planning, and Learningcited by 61
  10. 2025Design and Control of a Bipedal Robotic Charactercited by 18

Built on by

Cited by year

Citing papers per year: 2025: 1.

1 paper.

  1. 2025Robot Crash Course: Learning Soft and Stylized Fallingcited by 4

Similar, not cited

Close in wording, no citation either way

  1. 2025StyleLoco: Generative Adversarial Distillation for Natural Humanoid Robot Locomotion
  2. 2026Multi-Gait Learning for Humanoid Robots Using Reinforcement Learning with Selective Adversarial Motion Prior
  3. 2025Keep on Going: Learning Robust Humanoid Motion Skills via Selective Adversarial Training
  4. 2026Walk Like This: Stylized Locomotion for Physics-Based Characters through Example-Driven Diffusion
  5. 2021AMP: Adversarial Motion Priors for Stylized Physics-Based Character Control
  6. 2026Predictive Style Matching: Natural and Robust Humanoid Locomotion
  7. 2026Unified Walking, Running, and Recovery for Humanoids via State-Dependent Adversarial Motion Priors
  8. 2026T-GMP: Terrain-conditioned Generative Motion Priors for Versatile and Natural Humanoid Locomotion ↗