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This paper's most distinctive feature lies in its proposed Transformer Transformer model, which can directly participate in the robot's body design. Only by inputting a segment of conditional trajectory into the model can one generate a complete robot structure suitable for completing these actions, encompassing links, joints, motors, and inertial properties.

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01.

先建立一套通用的语言

把机器人的身体变成token

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完整性：

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灵活性：

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一致性：

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可扩展性：

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可优化性：

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▲ 用RoboTokens、text Token描述机器人硬件

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02.

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1、构型感知控制器

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▲ 同一控制器跟踪运动轨迹

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▲ 机器人的扩散生成

03.

动力学自引导：

输入奖励函数，输出机器人方案

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▲ Transformer Transformer预测状态和动作令牌

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04.

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▲ 奖励更高，速度快好几个数量级

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▲ 多轨迹双臂协同优化任务数据

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▲ 甩布测试

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与原始设计相比，优化方案发生了两项核心改变，该平台设置了更长的连杆长度，使手臂能够完整覆盖甩布轨迹，同时保持足够轻量，以确保关节载荷处于Dynamixel舵机/电机的扭矩包络线的安全范围内。

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05.

结语：编码、泛化、数据采集成本

三大技术难题仍需攻克

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目前，RoboTokens 仅支持基本几何体描述，还无法处理任意网格、可变形物体，且暂未对周围环境场景及接触目标进行编码；在未见过的机器人构型上，跨构型控制器和强化学习专家模型的相关性仅为皮尔逊系数0.53，虽有潜力但泛化能力仍存在短板。

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来源：斯坦福新成果：模型会造机器人了 | 具身研习社