{"ID":2873897,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.05581","arxiv_id":"2509.05581","title":"Learning to Walk in Costume: Adversarial Motion Priors for Aesthetically Constrained Humanoids","abstract":"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.","short_abstract":"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 hea...","url_abs":"https://arxiv.org/abs/2509.05581","url_pdf":"https://arxiv.org/pdf/2509.05581v1","authors":"[\"Arturo Flores Alvarez\",\"Fatemeh Zargarbashi\",\"Havel Liu\",\"Shiqi Wang\",\"Liam Edwards\",\"Jessica Anz\",\"Alex Xu\",\"Fan Shi\",\"Stelian Coros\",\"Dennis W. Hong\"]","published":"2025-09-06T03:52:10Z","proceeding":"cs.RO","tasks":"[\"cs.RO\",\"cs.AI\",\"eess.SY\"]","methods":"[\"Reinforcement Learning\"]","has_code":false}