MoMo: Dial Motion Mode in Robot Manipulation with Spatiotemporal Action Tokenization

AI 中文解读

苹果这项研究给机器人装上了“动作调节器”，让它们干同一件事也能变着花样来，比如搬杯子时既能轻拿轻放，也能快速利落。以前机器人的动作就像放录像带，一套动作从头到尾固定不变，没法根据物体材质或现场情况微调。MoMo框架相当于把动作拆成可拼装的“积木”，再用一个“风格旋钮”控制拼装方式，机器人就能自主学习出不同力道、速度和轨迹的干活方式。这项技术最实际的看点是柔性制造和居家服务。未来你家扫地机器人遇到木地板和瓷砖时会自动换“脚感”，工厂机械臂抓取鸡蛋和铁块也用不同“手法”。普通人不必懂编程，只需像调空调温度一样设定“柔和”或“高效”模式，机器人就能精准配合人的需求。这离“机器人真正走进生活”又近了一步。

To operate effectively across diverse contexts, robots must not only perform manipulation tasks accurately but also adapt how their actions unfold to the task, object, and interaction setting. We ask whether this execution-level variation can be learned as a reusable behavioral factor shared across tasks. We present MoMo, a two-stage imitation-learning framework consisting of a spatiotemporal action tokenizer and a behavior-cloning transformer that takes task and a continuous motion-mode condition as inputs. Across six real-robot manipulation tasks, varying this condition produces steady…