{"ID":2829510,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.12230","arxiv_id":"2512.12230","title":"Learning to Get Up Across Morphologies: Zero-Shot Recovery with a Unified Humanoid Policy","abstract":"Fall recovery is a critical skill for humanoid robots in dynamic environments such as RoboCup, where prolonged downtime often decides the match. Recent techniques using deep reinforcement learning (DRL) have produced robust get-up behaviors, yet existing methods require training of separate policies for each robot morphology. This paper presents a single DRL policy capable of recovering from falls across seven humanoid robots with diverse heights (0.48 - 0.81 m), weights (2.8 - 7.9 kg), and dynamics. Trained with CrossQ, the unified policy transfers zero-shot up to 86 +/- 7% (95% CI [81, 89]) on unseen morphologies, eliminating the need for robot-specific training. Comprehensive leave-one-out experiments, morph scaling analysis, and diversity ablations show that targeted morphological coverage improves zero-shot generalization. In some cases, the shared policy even surpasses the specialist baselines. These findings illustrate the practicality of morphology-agnostic control for fall recovery, laying the foundation for generalist humanoid control. The software is open-source and available at: https://github.com/utra-robosoccer/unified-humanoid-getup","short_abstract":"Fall recovery is a critical skill for humanoid robots in dynamic environments such as RoboCup, where prolonged downtime often decides the match. Recent techniques using deep reinforcement learning (DRL) have produced robust get-up behaviors, yet existing methods require training of separate policies for each robot morp...","url_abs":"https://arxiv.org/abs/2512.12230","url_pdf":"https://arxiv.org/pdf/2512.12230v1","authors":"[\"Jonathan Spraggett\"]","published":"2025-12-13T07:59:52Z","proceeding":"cs.RO","tasks":"[\"cs.RO\",\"cs.LG\"]","methods":"[\"Reinforcement Learning\"]","has_code":false,"code_links":[{"ID":605955,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2829510,"paper_url":"https://arxiv.org/abs/2512.12230","paper_title":"Learning to Get Up Across Morphologies: Zero-Shot Recovery with a Unified Humanoid Policy","repo_url":"https://github.com/utra-robosoccer/unified-humanoid-getup","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}