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

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 conditi

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ethics.ai (30 July 2026), “MoMo: Dial Motion Mode in Robot Manipulation with Spatiotemporal Action Tokenization,” evidence record 15182, https://ethics.ai/record/15182 (originally published by Apple Machine Learning Research).

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