TARS发布具身原生基础模型AWE 3.5
A lot of embodied AI still feels like AI modules bolted onto a robot.
具身智能领域的新架构尝试，强调原生一体化而非模块拼接，对关注机器人底层模型的开发者有参考价值。
A lot of embodied AI still feels like AI modules bolted onto a robot.
许多具身智能仍然感觉像是被硬塞到机器人身上的 AI 模块。
TARS is taking a different architectural bet with AI World Engine (AWE) 3.5, TARS’ embodied-native foundation model for physical AI.
TARS 正在通过 AI World Engine (AWE) 3.5 采取不同的架构策略，这是 TARS 面向物理 AI 的具身原生基础模型。
Its "Born as One" approach puts action, perception, geometry, and touch into one model from the beginning rather than stitching those capabilities together later.
其“Born as One”（天生一体）的方法从最初就将动作、感知、几何和触觉整合到一个模型中，而不是在后期将这些能力拼接在一起。
The same model-driven system is designed to generalize across different tasks, objects, environments and robot bodies.
同一个模型驱动的系统旨在跨不同任务、物体、环境和机器人本体实现泛化。
The training recipe then implements and validates a full closed-loop methodology for embodied-native foundation models through pre-training and post-training.
随后，训练配方通过预训练和后训练实施并验证了具身原生基础模型的完整闭环方法论。
During pre-training, 2 priors give the model a base understanding of action patterns, spatial structure and understanding of physical laws before it is adapted to a robot, while post-training uses the AI World Engine to roll possible future states forward inside the model, predict what different actions may lead to and use those predictions to choose better actions.
在预训练期间，两个先验知识赋予模型对动作模式、空间结构和物理定律的基础理解，使其在被适配到机器人之前具备这些认知；而后训练则利用 AI World Engine 在模型内部向前推演可能的未来状态，预测不同动作可能导致的结果，并利用这些预测来选择更优的动作。
TARS describes the full loop as 5 connected parts: embodied-native architecture, dual-prior pre-training, World Engine-driven post-training, scaling validation and continuous data feedback.
TARS 将整个闭环描述为五个相连的部分：具身原生架构、双先验预训练、World Engine 驱动的后训练、扩展验证以及持续的数据反馈。
TARS positions AWE 3.5 as one of the most powerful embodied-native foundation models for general-purpose physical AI, with several minutes of long-horizon closed-loop reasoning and roughly 2x task execution efficiency versus PI0.5.
TARS 将 AWE 3.5 定位为最强大的通用物理 AI 具身原生基础模型之一，具备数分钟的长视界闭环推理能力，且任务执行效率约为 PI0.5 的两倍。
@TARSRobotics #AWE35 #TARS #tarsrobotics
@TARSRobotics #AWE35 #TARS #tarsrobotics
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