四足机器人的行为基础：ABot-C0技术报告
Behavior Foundations for Quadruped Robots: ABot-C0 Technical Report
AI总结：
该研究聚焦四足机器人运动控制，提出ABot-C0通用系统，构建数据金字塔生成运动片段，训练流匹配通用策略展示跟踪缩放定律，采用特定框架推动全地形遍历运动，经实验验证其能让四足机器人迈向产品级行为智能。
摘要：
在具身智能系统中，运动控制器是语义推理与物理执行间的关键桥梁。人形控制借助大规模人体运动捕捉数据和运动跟踪范式取得快速进展。但四足机器人运动语料库的生成在可扩展性和物理可行性方面面临更多根本障碍，动物运动数据稀缺，跨实体重定向仍很脆弱。我们提出ABot-C0，这是一种用于四足机器人的通用运动控制系统，它建立了三个互补的行为基础：可扩展的多源运动数据管道、跨运动跟踪、 locomotion和场景交互的稳健策略学习，以及用于可靠实际操作的统一部署堆栈。从根本上说，我们通过条件视频生成合成、带注释的运动捕捉、遥操作和人工设计构建了一个数据金字塔，生成16074个物理上可行的运动片段作为各种运动学习需求的数据基础。然后，我们训练了一个流匹配通用策略，首次展示了四足运动跟踪缩放定律，即其性能随着训练规模的扩大而持续提高，具有零样本跟踪未见运动的能力。然后，我们通过采用具有时间激光雷达记忆和地形预测监督的三阶段特权到感知框架，进一步推动稳健的全地形遍历运动。总体而言，这些组件形成了一个运动通才，用于协调多策略执行、平滑行为过渡、节能控制和用于实际部署的安全机制。在城市地形自主导航和伴随式多模态交互上的广泛实验表明，四足机器人正从单功能演示迈向产品级行为智能。
英文摘要：
The motion controller is one of the most fundamental modules in embodied intelligence systems. Driven by large-scale human motion-capture data and the motion-tracking paradigm, humanoid control has achieved remarkable progress in recent years. However, migrating this recipe to the quadrupedal setting is far less straightforward: animal motion data is scarcer and harder to capture at scale than human data, and cross-embodiment retargeting remains fragile. We present ABot-C0, a generalist motion-control system for quadruped robots that establishes three complementary behavior foundations: a scalable multi-source motion-data pipeline, robust policy learning across motion tracking, locomotion, and scene interaction, and a unified deployment stack for reliable real-world operation. Fundamentally, we construct a data pyramid through conditional video-generation synthesis, annotated motion capture, teleoperation, and human design, producing 16,074 physically feasible motion clips as the data foundation for diverse motion-learning demands. With large-scale motion data, a Flow-Matching generalist policy demonstrates, for the first time, a scaling law for quadruped motion tracking: performance improves consistently as training scales up, with zero-shot capability to track unseen motions. We then go a step further toward robust all-terrain locomotion by adopting a three-stage privileged-to-perceptive framework with temporal LiDAR memory and terrain-predictive supervision. Collectively, these components form a motion generalist that coordinates multi-policy execution, smooth behavior transitions, energy-efficient control, and safety mechanisms for real-world deployment. Extensive experiments on urban-terrain autonomous navigation and companion-style multimodal interaction demonstrate that quadruped robots can move beyond functional demos toward product-level behavioral intelligence.
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