H-Zero：跨人形机器人运动预训练实现少样本新躯干转移
H-Zero: Cross-Humanoid Locomotion Pretraining Enables Few-shot Novel Embodiment Transfer
AI总结：
H-Zero通过跨人形机器人预训练实现少样本新躯干转移，提升机器人运动控制的通用性和效率。
摘要：
人形机器人技术的快速发展加剧了对稳健且适应性强的控制器的需求，以实现在多样化平台上的稳定和高效运动。然而，开发此类控制器仍是一个重大挑战，因为现有解决方案针对特定机器人设计，需要为每个躯干进行大量调整奖励函数、物理参数和训练超参数。为了解决这一挑战，我们引入了H-Zero，一种跨人形机器人的运动预训练流程，学习通用的人形基础策略。我们证明，预训练在有限的躯干集上能够实现零样本和少样本转移至新的机器人，只需最小的微调。评估显示，预训练策略在模拟中能保持高达81%的完整回合持续时间，同时在30分钟微调内实现对未见人形机器人和直立四足机器人的少样本转移。
英文摘要：
The rapid advancement of humanoid robotics has intensified the need for robust and adaptable controllers to enable stable and efficient locomotion across diverse platforms. However, developing such controllers remains a significant challenge because existing solutions are tailored to specific robot designs, requiring extensive tuning of reward functions, physical parameters, and training hyperparameters for each embodiment. To address this challenge, we introduce H-Zero, a cross-humanoid locomotion pretraining pipeline that learns a generalizable humanoid base policy. We show that pretraining on a limited set of embodiments enables zero-shot and few-shot transfer to novel humanoid robots with minimal fine-tuning. Evaluations show that the pretrained policy maintains up to 81% of the full episode duration on unseen robots in simulation while enabling few-shot transfer to unseen humanoids and upright quadrupeds within 30 minutes of fine-tuning.
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。
