OFlow: Injecting Object-Aware Temporal Flow Matching for Robust Robotic Manipulation

• URL: http://arxiv.org/abs/2604.17876v1
• Date: Mon, 20 Apr 2026 06:38:01 GMT
• Title: OFlow: Injecting Object-Aware Temporal Flow Matching for Robust Robotic Manipulation
• Abstract summary: VLA models typically act only on the current frame, while future prediction and object-aware reasoning are often learned in separate latent spaces.<n>We propose OFlow, a framework that addresses both limitations by unifying temporal foresight and object-aware reasoning in a shared semantic latent space.<n>Our method forecasts future latents with temporal flow matching, factorizes them into object-aware representations that emphasize physically relevant cues.
• Score: 55.56490813640669
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Robust robotic manipulation requires not only predicting how the scene evolves over time, but also recognizing task-relevant objects in complex scenes. However, existing VLA models face two limitations. They typically act only on the current frame, while future prediction and object-aware reasoning are often learned in separate latent spaces. We propose OFlow (injecting Object-Aware Temporal Flow Matching into VLAs), a framework that addresses both limitations by unifying temporal foresight and object-aware reasoning in a shared semantic latent space. Our method forecasts future latents with temporal flow matching, factorizes them into object-aware representations that emphasize physically relevant cues while filtering task-irrelevant variation, and conditions continuous action generation on these predictions. By integrating OFlow into VLA pipelines, our method enables more reliable control under distribution shifts. Extensive experiments across LIBERO, LIBERO-Plus, MetaWorld, and SimplerEnv benchmarks and real-world tasks demonstrate that object-aware foresight consistently enhances robustness and success.

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