zeromq python 原力灵机发布通用体现基础模型DM0.5、通用机器人本体Apex等新品

7月9日, 在北京, 举行了原力灵机开发者大会, 原力灵机发布了五大新品, 有通用具身基础模型DM0.5, 通用机器人本体Apex, 还有全面支持模型走向规模化应用的开发者平台, 其中包括DFOL2.0、MaaS、DexOS三件套, 现场还深度展示了面向真实生产场景的解决方案的最新产业实践。

原力灵机CEO唐文斌讲道, 具身智能行业处在一个至关重要的关键拐点之上, 模型能力正从完成特定任务朝着真正的泛化加速迈进, 这是继大模型之后AI能力出现的又一次质变, 然而从模型能力迈向真实生产力, 行业还得跨越具身模型应用的门坎。持续的up, 需要促使数据从“采集型”向“场景型”实现跃迁, 这要有更强大的通用模型以及系统化的落地能力给予支持。

据悉, 此次全新推出的 DM0.5, 明确的定位是针对面向开放世界的通用具身基础模型, 也就是 Model。相较于上一代 DM0, DM0.5 在训练的数据规模方面, 在架构创新层面, 以及开放世界的泛化能力等诸多方面, 达成了全面的提升。它构建起了涵盖导航、抓取、全身控制这三大任务, 以及六类机器人本体的庞大的数据资产。在 7 月 9 日之前, DM0.5 在开放代码仓库进行了相关操作, 同时上线了模型权重；到了 7 月 15 日, 其核心代码被提交至社区, 并且技术报告也进行了同步公开。

原力灵机持有这样的观点, 具身智能这个行业正处在一个关键的转折点上, 这个转折点是从“模型能力”迈向“真实生产力”的, 模型若要切实进入产业应用领域, 仅仅依靠模型和本体是不行的, 在这中间还需要一套具备工程化、系统化以及平台化特点的支撑层。

其中, DFOL2.0是一个闭环强化学习框架, 该框架由世界模型驱动。它要解决“模型怎样才能变强”这个问题, 是以全新发布的通用具身形世界模型DW0.5作为高保真仿真器的。它把强化学习“放入”虚拟世界之中, 使模型在仿真里批量进行试错, 借此持续进化, 且不再依靠昂贵的真机集群来反复迭代。在这一框架的驱动之下, 真机训练数据的需求降低了60%, 而整体的训练成本下降了40%。

DexOS作为它的首个具身通用操作系统, 去解决“模型怎样才能跑得稳”这个问题, 把大模型跟机器人硬件之间的“最后一公里”打通, 开发者只要写上几行代码, 便能够完成机器人连接、感知获取、模型推理以及动作执行, 不必要为每一款新的机型再去重新适配底层硬件。当前, 原力灵机已经跟天工、华勤、史河等知名机器人本体厂商共同构建生态。

具身通用型的MaaS服务之所“能力怎样能够被规模化地调用这一问题”, 它提供的是一体的具身大模型服务, 它支持借助“一行命令”来开启Zero-shot之推理, 它同样支持借由可视化的业务应用环境来顺利达成约定的标准化子领域与智能化设计策略的在线培训以及依照规定方式将软件程序或其他电脑系统安装到计算机上的操作, 并且它采用的是依据实际使用量来计算费用的方式, 极大程度地压低了企业接入的限制条件。

透过DFOL 2.0促使模型进化, DexOS达成软硬件深度解耦, MaaS把支撑能力进行规模化部署, 这三者构筑起驱动具身智能进化的动力系统, 得以让DM0.5这般的通用基础模型切实由实验室迈向产业场景。

此外, 存在这样一个系统, 它是具身智能面向真实场景的多智能体混合作业系统, 同时也是具身智能进入真实产业场景的系统化解决方案。该系统在目标、模型、多形态硬件与安全边界之间构建了一套调度机制, 这套机制具备可保护、可验证、可闭环的特性, 借助该机制能使机器人在受保护的状态下顺利进入真实场景, 进而达成持续、高频的作业以及数据回流。

在底层能力方面, 依靠DM系列模型以及-VLA来进行构建, 把视觉、语言、空间推理还有动作生成整合、统一到单个具身基础模型里, 经过综合评测, 其成功率在全球范围内位居第一；-VLA达成了30Hz的多视角推理帧率, 以及480Hz的最高轨迹频率, 其响应节拍接近于人工操作的水平。

在任务分级以及容错这块儿, 依据业务特征进行分层协同, 其中低成本硬件用以覆盖简单且高频的任务, 具身大模型致力于攻克异形、软包等复杂又有长尾特性的场景, 另外, 遇到异常情况时会自动重试, 或者实施安全回退, 又或者流转至人工接管, 以此保证生产线不会因为局部出现异常而导致停摆。

应用场景方面, 主要致力于解决中小件商品的“货到机器人”折分挑选难题, 能够适应ToC电商仓, 也就是爆款和订单选择那种情况，还能够兼容ToB流通仓, 就是门店补充货品时整箱拆分那种情形, 另外也可以契合逆向物流, 即退货检查验证和上架这种状况, 存在三大关键仓储物流场景。

借助多设备协同来使综合部署成本得以降低, 进而形成“经济飞轮”。规模化部署所带来的高频真实数据给模型进化予以反哺, 以此形成“数据飞轮”, 在双轮驱动的状况下朝着规模化交付迈进。当下, 已在某行业头部客户的真实仓库达成实地测试, 场景含括超十四万SKU、每日订单峰值到达数万单。

此外, Apex是那种具身原生的通用机器人, 其最大的特点在于已充分思考并顾及具身模型的应用特点, 以及多场景使用需求设计。WWw.Baidu.Meyhvy.cN/Article/details/99228.sHtML
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