高德发布两款ABot系列基座模型，达成全球首个具身操作和具身导航"双SOTA"

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近日，阿里巴巴集团旗下高德正式发布具身操作基座模型ABot-M0与具身导航基座模型ABot-N0，补齐了具身机器人规模化落地的两块核心能力——操作的通用性和导航的长程性，并刷新了全球多项权威评测纪录，高德也成为全球首个在具身导航与具身操作上同步达到SOTA（目前最好、最先进的模型）的厂商。ABot-M0：全球首个统一架构的机器人基础模型，让机器人拥有"通用大脑"长期以来，机器人技术的规模化应用面临...

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基准上达到了 80.5%，较业界先进方案pi0提升近30%，展现了其在高扰动高难度具身操作任务中的领先性能。ABot-M0在Libero-Plus的评测通过系统的工程设计与算法改进，ABot-M0 探索了一条通向通用具身智能的技术路径，也为未来开放、共享的机器人生态提供了可复现、可扩展的基础支持。ABot-N0：全球首个五大导航任务统一的全栈导航基座模型导航是机器人进入物理开放世界的核心基础能力，机器人需要在动态且存在干扰的环境里展现出通用的行动能力，如跨场景送物或跟随服务，这同时也是具身智能演进的终极命题。然而，当前的具身导航研究普遍深陷“碎片化”：主流方法往往针对特定任务构建孤立的专用架构，这不仅限制了模型的跨任务泛化能力，更阻碍了智能体从海量异构数据中提取统一物理先验的可能性。这也是当前机器人常陷于“环境看不懂、动作做不准”的核心原因，复杂指令（如“去门口帮我看看快递”）更是难以执行。高德推出的具身导航基座模型ABot-N0，以“全任务一统”为核心目标，并实现全球首次在单一模型中完整集成Point-Goal（点位导航）、Object-Goal（目标导航）、Instruction-Following（指令跟随）、POI-Goal（兴趣点导航）与Person-Following（人物跟随）五大导航任务，有效突破了传统架构中任务割裂的瓶颈。比如，当用户对搭载ABot-N0的具身机器人说：“带我去奶茶店买一杯奶茶，再帮我占个座。”时系统会自动分解为具体的导航任务：首先执行Point-Goal，根据地图记忆接近奶茶店区域；之后切换至POI-Goal，精准锁定店铺入口并靠近；随后触发Instruction-Following，进入店铺并导航至柜台；最后执行Object-Goal，在店内寻找空沙发并停靠。相较于只能支持部分任务的具身导航模型，高德ABot-N0所实现的五大导航任务给长程复杂任务的执行提供了可行的解决方案。这一能力背后，是高德在架构设计、数据引擎和系统框架上的关键革新。ABot-N0的数据、性能、任务概览在模型架构上，ABot-N0采用层次化的“大脑‑动作”设计哲学：由“认知大脑”理解指令并做推理，由基于流匹配（Flow Matching）的“动作专家”生成精确且多峰分布的连续轨迹 。训练上，先让模型做认知训练热身，再用部分认知数据和海量导航动作进行联合监督微调，最后用强化学习把导航决策对齐到人类偏好的行为价值，最终打造出真实环境中更通用的VLA基座模型。在数据侧，依托高德长期沉淀的场景资产和专家示例，高德构建了业内最大规模的具身导航数据引擎，涵盖约 8000 个高保真 3D 场景等海量时空数据与近1700万条专家示例，从而增强模型在真实环境中的泛化能力与鲁棒性。基于ABot-N0的系统性创新，其在CityWalker、SocNav、R2R-CE/RxR-CE、HM3D-OVON、BridgeNav、EVT-Bench七大权威基准测试中全面刷新了世界纪录。其中在SocNav闭环仿真中，成功率（SR）飙升40.5%，在HM3D-OVON评测中成功率（SR）提升8.8%，均显著强于之前的SOTA模型。Point-Goal任务：在CityWalker及SocNav上分别进行开环和闭环评测此外，为了解决机器人在执行长程复杂任务时的任务拆解与容错问题，高德提出了可落地的Agentic Navigation 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