Embodied Intelligence Breakthrough: Amap Fully Open-Sources Universal Robot Base Model ABot-M0

marsbit發佈於 2026-04-01更新於 2026-04-01

文章摘要

Embodied Intelligence Breakthrough: AutoNavi Open-Sources Universal Robot Base Model ABot-M0 AutoNavi has announced the full open-source release of ABot-M0, the world's first unified architecture-based embodied manipulation base model. This model is designed to enable "one general brain to adapt to multiple forms of robots," aiming to break down barriers between heterogeneous hardware and accelerate the adoption of embodied intelligence in industrial and household settings. ABot-M0 demonstrated exceptional performance in industry tests, achieving a task success rate of 80.5% on the Libero-Plus benchmark—a nearly 30% improvement over the previous benchmark, Pi0. It also set new state-of-the-art records on benchmarks like Libero and RoboCasa. The open-source release addresses long-standing challenges in the field, such as data isolation and deployment difficulties, by providing resources across three key dimensions: - **Data:** The UniACT dataset, the largest of its kind, with over 6 million real operation trajectories and full data pipeline tools. - **Algorithm:** The model architecture and training framework, featuring innovative components like Action Manifold Learning (AML) and a dual-stream perception architecture. - **Model:** End-to-end pre-trained models and a complete toolchain for out-of-the-box deployment, significantly lowering the barrier to adaptation. According to AutoNavi's ABot-M0 technical lead, this open-source initiative aims to build a bridge between ac...

The field of embodied intelligence has reached a milestone. Amap today officially announced the full open-source release of the world's first unified architecture-based robot operation base model ABot-M0. The core positioning of this model is to achieve "one universal brain adaptable to various forms of robots," aiming to break down barriers between heterogeneous hardware and accelerate the transition of embodied intelligence from the laboratory to industrial and home scenarios.

Core Technology and Performance

ABot-M0 has demonstrated outstanding performance in multiple industry authoritative benchmark tests. Data shows that the model achieved a task success rate of up to 80.5% on the Libero-Plus benchmark, a nearly 30% improvement over the previous industry benchmark solution Pi0. Furthermore, it set new SOTA (State-of-the-Art) records in tests such as Libero and RoboCasa.

Full Open-Source Across Three Dimensions

To address the long-standing pain points of "data silos" and "deployment difficulties" in the field of embodied intelligence, Amap's open-source release covers three key dimensions: underlying data, core algorithms, and pre-trained models:

  • Data Level: Open-sourced the currently largest universal robot dataset UniACT. This dataset integrates over 6 million real operation trajectories and provides a complete processing pipeline from heterogeneous data to standardized training data.

  • Algorithm Level: Simultaneously released the model architecture and training framework. Core highlights include Amap's innovative Action Manifold Learning (AML) algorithm and Dual-Stream Perception Architecture, endowing robots with exceptional spatial understanding and action execution capabilities.

  • Model Level: Provided end-to-end pre-trained models and a complete toolchain. Developers can achieve "out-of-the-box" usability without building a framework from scratch, significantly lowering the barrier to adapting to industrial collaborative or home service robots.

Industry Impact

Amap's ABot-M0 technical lead stated that true general embodied intelligence requires the collective refinement of global developers. The open-sourcing of ABot-M0 is not just a sharing of technology but also aims to build a bridge connecting academic research and industrial application, enabling every robot of different forms to possess a smart, reliable, and universal "brain".

相關問答

QWhat is the name of the general-purpose robotic base model that AutoNavi has fully open-sourced?

AThe model is called ABot-M0.

QWhat is the core positioning or main goal of the ABot-M0 model?

AIts core positioning is to achieve 'one general-purpose brain adapted to various forms of robots', aiming to break down barriers between heterogeneous hardware.

QWhat is the name of the large-scale dataset that was open-sourced alongside the model, and how many real operation trajectories does it contain?

AThe dataset is called UniACT, and it integrates over 6 million real operation trajectories.

QOn which benchmark did ABot-M0 achieve a task success rate of 80.5%, and what was the performance improvement over the previous benchmark solution?

AIt achieved an 80.5% success rate on the Libero-Plus benchmark, which is a nearly 30% improvement over the previous benchmark solution, Pi0.

QName two core technical highlights of the algorithm that were open-sourced.

AThe two core technical highlights are the Action Manifold Learning (AML) algorithm and the Dual-Stream Perception architecture.

你可能也喜歡

奥特曼承认:高估了AI抢饭碗!黄仁勋:失业论完全搞反了

2025年10月,OpenAI CEO山姆·奥特曼曾预言可能出现由AI管理的大公司。然而在2026年7月的播客中,他改口称人们“并不真正想要一个AI CEO”,因为公司决策需要明确的责任归属和真人间的信任。他承认自己高估了AI消灭初级白领岗位的速度,“就业末日”大概率不会到来。 几乎同时,英伟达CEO黄仁勋在YC创业课上指出,“AI毁掉工作”的叙事完全搞反了。他认为,AI替代的是工作中的具体“任务”,而非整个“工作”。例如放射科医生和软件工程师,虽然AI承担了更多读片和写代码任务,但这些岗位的数量反而在增长,因为效率提升后业务规模扩大,产生了更多对沟通、判断、协调等AI无法替代的人类技能的需求。 马里兰大学与LinkUp的数据显示,截至2025年第四季度,美国整体招聘需求未被AI压垮,明确面向应届生的岗位比例甚至有所回升。报告指出,年轻员工可能更受益于AI工具,它能快速提供经验,使其变得“便宜又好用”。 然而,挑战依然存在:AI最先接管的正是数据录入、基础分析等标准化入门任务,这使得新人积累初期经验的传统路径变窄,入门台阶正在升高。 两位领袖的观点共同揭示了一个趋势:AI越强大,人类工作的核心价值就越向承担责任、建立信任、做出最终判断等层面集中。这些无法被机器替代的部分,构成了个人真正的职业护城河。

marsbit1 小時前

奥特曼承认:高估了AI抢饭碗!黄仁勋:失业论完全搞反了

marsbit1 小時前

每周编辑精选 Weekly Editor's Picks(0725-0731)

**每周编辑精选(0725-0731)摘要** 本文筛选深度分析,滤除资讯噪音,带来一周核心洞察。 **宏观局势**:美联储迎来近年“最不确定”会议。尽管经济数据为等待提供空间,但高通胀、地缘风险及官员鹰派表态,令市场无法完全排除加息风险,并已为此付费。 **投资与创业**: * 加密投资是长期心态博弈,获胜者需看清资产本质、确信趋势并能承受深度回撤。建议长线布局比特币与优质公链。 * 全球股市(尤科技股)呈现“币圈化”:叙事压倒估值,杠杆放大情绪,社交媒介加速共识极端化。 * Hyperliquid、Polymarket等龙头平台的跨界尝试遇阻,核心难点在于复制原有赛道的用户习惯与流动性深度。 * 多个加密协议收入增长但代币价格不涨,原因在于内部抛压、负面情绪及竞争。好协议不等于好代币,需审视收入、分配与释放机制。 **AI与存储**: * 英伟达信用违约率暴涨,反映市场对AI云设施扩张风险的定价。中国芯片产业崛起正撼动全球存储定价逻辑。 * 存储板块“一夜惊魂”是基本面与预期面脱节,市场已开始为2027年潜在供给过剩提前定价。 * AI烧钱凶猛,市场耐心受考验。多空分歧在于:需求真实但供给受限 vs. 未来回报可见度低。 * SK海力士虽录得史上最赚钱季度,但股价仍“不及预期”,显示市场对其未来增长空间的定价存在分歧。 **政策与稳定币**:美国《Clarity法案》推进至最后阶段,但道德条款等关键分歧仍存,且需与其他争议法案争夺有限表决时间,年内落地概率被下调。若未通过,对加密市场冲击或有限,但将增加未来立法难度。 **CeFi & DeFi**:Ondo代币近期上涨,源于其在链上交易美股主线动作密集,既占据上游代币化资产份额,又向下游拓展保证金应用。但受制于整体市场颓势,涨势更多是短期资金博弈。 **以太坊与扩容**:Lido正启动将800多万枚ETH迁移至Pectra升级后的新型验证器架构,这代表了staking资本管理效率的结构性提升,但不会直接降低用户Gas费用。当前ETH价格走弱,部分源于投资者对其价值增长逻辑感到困惑。 **其他要点**:TradeXYZ平台对A股新股定价展现高精准度;Pons平台币半月暴涨登顶Robinhood Chain;币印破产案例警示平台钱包并非资产托管;一周热点还包括美联储按兵不动、MiCA落地欧洲、长鑫科技上市创纪录、OpenAI称未来12个月将“震撼世界”等。

marsbit2 小時前

每周编辑精选 Weekly Editor's Picks(0725-0731)

marsbit2 小時前

交易

現貨
活动图片