SONIC：为自然人形全身体控进行超大规模运动追踪
SONIC: Supersizing Motion Tracking for Natural Humanoid Whole-Body Control
AI总结：
本文提出了一种超大规模运动追踪方法，通过扩大模型容量、数据和计算资源，实现了一种能够产生自然且稳健全身体态的通用人形控制器，并展示了其在运动追踪任务中的可扩展性及在下游任务中的应用价值。
摘要：
尽管大规模基础模型在数千块GPU上训练已取得显著进展，但类似规模提升在人形控制中尚未显现。当前的人形神经控制器规模较小，仅针对有限的行为集，并在少量GPU上训练。我们证明，扩大模型容量、数据和计算资源可以产生一个通用的人形控制器，能够实现自然且稳健的全身体态。我们将运动追踪定位为人形控制的可扩展任务，利用密集监督的多样化动作捕捉数据获取人类运动先验知识，而无需手动奖励工程。我们通过沿三个轴扩展构建了一个运动追踪的基础模型：网络大小（120万到4200万参数）、数据集规模（10亿+帧来自700小时的动作捕捉数据）以及计算资源（21000 GPU小时）。除了展示规模优势外，我们还通过：（1）实时运动规划器连接运动追踪到导航等任务，实现自然和交互式控制；（2）统一的token空间支持VR远程操作和视觉-语言-动作（VLA）模型，使用单一策略。通过这一接口，我们展示了需要协调手和脚放置的自主VLA驱动全身体控。扩大运动追踪表现出有利的特性：性能随计算和数据多样性稳步提升，学习的策略能泛化到未见的运动，使大规模运动追踪成为人形控制的实用基础。
英文摘要：
Despite the rise of billion-parameter foundation models trained across thousands of graphical processing units (GPUs), similar scaling gains have not been shown for humanoid control. Current neural controllers for humanoids remain modest in size, target a limited set of behaviors, and are trained on a handful of GPUs. We show that scaling model capacity, data, and compute yields a generalist humanoid controller capable of natural, robust whole-body movements. We position motion tracking as a scalable task for humanoid control, leveraging dense supervision from diverse motion-capture data to acquire human motion priors without manual reward engineering. We build a foundation model for motion tracking by scaling along three axes: network size (1.2M to 42M parameters), dataset volume (100M+ frames from 700 hours of motion capture), and compute (21k GPU hours). Beyond demonstrating the benefits of scale, we further show downstream utility through a real-time kinematic planner that bridges motion tracking to tasks such as navigation, enabling natural and interactive control, as well as a unified token space that supports virtual reality (VR) teleoperation and vision-language-action (VLA) models with a single policy. Through this interface, we demonstrate autonomous VLA-driven whole-body loco-manipulation requiring coordinated hand and foot placement. Scaling motion tracking exhibits favorable properties: performance improves steadily with compute and data diversity, and learned policies generalize to unseen motions, establishing motion tracking at scale as a practical foundation for humanoid control.
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。
