TEMPO: Learning Temporal Context for Dynamic Robot Manipulation
TEMPO is proposed, which augments a pretrained VLA with two temporal inputs: a motion summary extracted from a frozen video foundation model to resolve motion ambiguity and a compact proprioceptive history to resolve state aliasing.
Zhen-Yang Feng, Jimin Heo, Erik B. Sudderth et al.
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