学习跨形态恢复：零样本恢复与统一人形政策
Learning to Get Up Across Morphologies: Zero-Shot Recovery with a Unified Humanoid Policy
AI总结：
本文提出一种统一的人形政策，能够跨七种不同形态的机器人实现零样本跌倒恢复，通过CrossQ训练在未见形态上达到86%的转移率，消除了对特定形态训练的需求。
摘要：
跌倒恢复是人形机器人在动态环境中（如RoboCup）的关键技能，长时间停机往往决定比赛结果。最近使用深度强化学习（DRL）的技术产生了稳健的起身行为，但现有方法需要为每种机器人形态训练单独的策略。本文提出了一种单一的DRL策略，能够跨七种人形机器人（高度0.48-0.81米，重量2.8-7.9公斤，动力学不同）恢复跌倒。通过CrossQ训练，统一策略在未见过的形态上实现了高达86±7%（95%置信区间[81,89]）的零样本转移，消除了对机器人特定训练的需求。全面的leave-one-out实验、形态缩放分析和多样性消融实验表明，针对形态的覆盖提高了零样本泛化能力。在某些情况下，共享策略甚至超过了专门的基线。这些发现展示了对跌倒恢复的形态无关控制的实用性，为通用的人形控制奠定了基础。软件是开源的，可在https://github.com/utra-robosoccer/unified-humanoid-getup获取。
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
