Behavior Foundations for Quadruped Robots: ABot-C0 Technical Report

• URL: http://arxiv.org/abs/2607.07370v2
• Date: Thu, 09 Jul 2026 06:14:09 GMT
• Title: Behavior Foundations for Quadruped Robots: ABot-C0 Technical Report
• Abstract summary: We present ABot-C0, a generalist motion-control system for quadruped robots.<n>We construct a data pyramid through conditional video-generation, annotated motion capture, and human design, producing 16,074 physically feasible motion clips.<n>We then go a step further toward robust all-terrain locomotion by adopting a three-stage privileged-to-perceptive framework.
• Score: 25.18747802029395
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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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