arXiv (OAI Expanded)
open access
TEMPO: Learning Temporal Context for Dynamic Robot Manipulation
Vision-language-action (VLA) models have achieved impressive performance in quasi-static manipulation, but struggle in dynamic manipulation tasks because they operate on a single observation at inference time. We identify two representational failures that underlie this limitation. The first is moti
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