编辑｜Panda 在数字世界里，AI 智能体正通过 MoltBook 这样社交网络进行语义协商并协同进化，而在物理世界中，具身智能也迎来了一次里程碑式的进展。 在原力灵机的实验室里，一台由 Hugging Face 开源的、3D 打印出来的 SO-101 机器臂正灵巧地将外形各异的物品放入指定的盒子中。这个动作看似简单，实则包 含了极高频的视觉反馈、力度感知以及对复杂物理环境的 直觉 判断。 实际上，这种从「计算」到「直觉」的跨越，并非源于针对这个特定硬件的繁琐调优，而是受益于一套标准化的底层基建。 在 2 月 10 日的技术开发日上，原力灵机正式发布了 开源具 身原 生框架 Dexbotic 2.0，并宣布了其与清华大学和无问芯穹支持的强化学习框架 RLinf 的战略合作 原力灵机合伙人汪天才将 Dexbotic 2.0 与 RLinf 的深度结合定义为 具身智能行业的「PyTorch 时刻」 。正如 PyTorch 通过标准化的张量计算与自动微分机制释放了 深度学习的生产力，Dexbotic 2.0 与 RLinf 的联手试图在具身智能这个碎片化严重的赛道上，建立一套通用的底座和基础设施。 伴随这一框架升 ...

Core Insights - The article highlights the launch of DM0, the world's first embodied native large model developed by Yuanli Lingji, which is trained from scratch and integrates multimodal internet information along with unique embodied scenario data such as driving behavior, robotic operations, and navigation [1] Group 1 - DM0 is trained from the ground up and incorporates data from various modalities, including driving behavior and robotic operations [1] - The model has undergone a pre-training phase that includes mixed tasks of system navigation, full-body control, and core task execution, covering eight significantly different machine types [1] - DM0 achieved first place in both single-task and multi-task evaluations in the RoboChallenge real machine assessment, currently ranking as the top model globally [1]