Impact of Multimodal Alignment on VLA Robustness Against Visual Distractors in Long-Horizon Manipulation on CALVIN

Abstract

Abstract: Vision-Language-Action (VLA) models have become a cornerstone in robotic policy learning, leveraging large-scale multimodal data for robust and scalable control. However, existing VLA frameworks primarily address short-horizon tasks, and their effectiveness on long-horizon, multi-step robotic manipulation remains limited due to challenges in skill chaining and subtask dependencies. In this work, we introduce Long-VLA, the first end-to-end VLA model specifically designed for long-horizon robotic tasks. Our approach features a novel phase-aware input masking strategy that adaptively segments eac

Research Question

To what extent do multimodal alignment techniques in VLA models improve robustness against visual distractors in long-horizon manipulation sequences evaluated on the CALVIN dataset?

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