[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-84220-en":3,"doc-seo-84220-105":29,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},84220,962075114101,"Seraphina","https://ap-avatar.wpscdn.com/avatar/e000253a75eb197efd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780044092746381165",8,"Research & Report","Behavior Foundations for Quadruped Robots: ABot-C0 Technical Report","The motion controller is a core module for embodied intelligence, yet adapting humanoid control recipes to quadruped robots is hindered by scarce animal motion data and fragile cross-embodiment retargeting. This report introduces ABot-C0, a generalist quadruped motion-control system built on three foundations: a scalable multi-source motion-data pipeline, robust policy learning across tracking, locomotion, and scene interaction, and a unified deployment stack. It constructs a data pyramid generating 16,074 physically feasible clips via video synthesis, annotated MoCap, teleoperation, and human design, enabling scaling laws and improved zero-shot tracking, plus terrain-robust all-terrain locomotion using temporal LiDAR memory and terrain-predictive supervision for real-world safe behavior.","arXiv :2607 .07370v2 [ cs .RO] 9 Jul 2026  \nBehavior Foundations for Quadruped Robots: ABot-C0  \nTechnical Report  \nXufeng Zhao∗ , Fuzhi Yang∗ , Jianhui Chen∗ , Li Gao , Zhang Meng , Jie Gao , Yao Zheng , Congyang Zhao , Tianxiong Lv , Menglin Yang , Minqi Gu , Yaru Zhao , Wenyu Liu , Honglin Han , Shihui Su , Zixiao Tang ,  \nLiu Liu‡, Mu Xu , Yang Cai‡, Wenbin Tang  \nAmapbot Team, Amap, Alibaba Group  \nAbstract  \nThe 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.  \n1 Introduction  \nMotion control, the ability to translate high-level intent into physically feasible whole-body movements while maintaining stability, adaptability, and safety throughout physical interaction with the environment, is fundamental to realizing general-purpose robots in real-world applications. In recent years, reinforcement  \nlearning has become the dominant paradigm in this domain because of its flexibility and scalability [21, 35 , 43], and the rapid maturation of physics simulators [3, 28 , 31 , 33 , 42] has further accelerated progress by enabling ∗ Equal contribution.  \n‡Corresponding author: {diana.ll, [yangcai.cy}@alibaba-inc.com](yangcai.cy}@alibaba-inc.com)  \nGeneralist Motion Control System  \n●  \n●  \n●  \n\n|  |  |\n| --- | --- |\n| Simulation\u003Cbr>Real-world ready Minimal sim2real gap Reliable | Robot I/O\u003Cbr>Shared Interface |\n\nInteraction  \nHuman Robot Interaction Dynamic Scene Navigation   \nABot-C0  \n● Fast iteration  \n● Safe sim2sim testing  \n● Scalable  \n4  Sim2Sim / Sim2Real  \nRecovery  \nPerturbation  \nFigure 1 Overview of ABot-C0. ABot-C0 is organized as a generalist quadruped motion-control system built on a scalable data pyramid, a three-task learning suite for locomotion, motion tracking, and scene interaction, and a unified deployment layer that supports smooth policy switching, recovery, and perturbation robustness. Sim-to-sim verification precedes sim-to-real transfer through a shared robot interface, enabling extensible applications such as human-robot interaction and dynamic-scene navigation.  \nmassively parallel policy optimization and narrowing the sim-to-real gap. Inspired by scaling laws in large language models","cbCaiqeR62tjZzm4","https://ap.wps.com/l/cbCaiqeR62tjZzm4","pdf",12206302,1,29,"English","en",105,"# Introduction\n# Generalist Motion Control System\n## Sim2Sim / Sim2Real\n## Robot I/O Shared Interface\n# Data Engine and Motion-Data Pyramid","[{\"question\":\"What is the main objective of ABot-C0?\",\"answer\":\"ABot-C0 aims to provide a generalist motion-control system for quadruped robots, unifying motion tracking, locomotion, and scene interaction into a single reliable stack for real-world operation.\"},{\"question\":\"How does ABot-C0 address the scarcity of quadruped motion data?\",\"answer\":\"It builds a scalable multi-source motion-data pyramid that combines conditional video generation, annotated motion capture, teleoperation, and human-designed motions, resulting in 16,074 physically feasible motion clips.\"},{\"question\":\"What techniques enable robust locomotion across different terrains?\",\"answer\":\"The report adopts a three-stage privileged-to-perceptive framework with temporal LiDAR memory and terrain-predictive supervision to improve all-terrain robustness.\"}]",1784194109,73,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":27},"behavior-foundations-for-quadruped-robots-abot-c0-technical-report","",{"@graph":35,"@context":85},[36,53,68],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/behavior-foundations-for-quadruped-robots-abot-c0-technical-report/84220/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-17","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What 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