MoMo: Dial Motion Mode in Robot Manipulation with Spatiotemporal Action Tokenization
MoMo is a two-stage imitation-learning framework that enables robots to adjust their motion mode (steady, dynamic, intermediate) during manipulation tasks. Using a spatiotemporal action tokenizer and a behavior-cloning transformer, the method takes task and a continuous motion-mode condition as inputs. Experiments on six real-robot tasks demonstrate compositional generalization to unseen task-mode combinations, showing that motion mode can be reused across tasks.
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[Submitted on 28 Jul 2026]
Title:MoMo: Dial Motion Mode in Robot Manipulation with Spatiotemporal Action Tokenization
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Abstract: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 \textbf{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, dynamic, and intermediate behaviors that human raters can distinguish and that differ in joint speed, acceleration, and end-effector approach pitch. On tasks demonstrated in only one mode, MoMo transfers the unseen requested mode while largely preserving task success. Together, these results provide evidence of compositional generalization to unseen task--mode combinations and show that motion mode can be reused across tasks to control how a manipulation skill is performed.
Subjects:
Robotics (cs.RO)
Cite as: arXiv:2607.26315 [cs.RO]
(or arXiv:2607.26315v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2607.26315
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Yuhan Hu [view email] [v1] Tue, 28 Jul 2026 22:25:10 UTC (4,107 KB)
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