Learning to Get Up Across Morphologies: Zero-Shot Recovery with a Unified Humanoid Policy

• URL: http://arxiv.org/abs/2512.12230v1
• Date: Sat, 13 Dec 2025 07:59:52 GMT
• Title: Learning to Get Up Across Morphologies: Zero-Shot Recovery with a Unified Humanoid Policy
• Abstract summary: Fall recovery is a critical skill for humanoid robots in dynamic environments such as RoboCup.<n>Recent techniques using deep reinforcement learning have produced robust get-up behaviors.<n>This paper presents a single DRL policy capable of recovering from falls across seven humanoid robots.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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

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