Long-VLA: Unleashing Long-Horizon Capability of Vision Language Action Model for Robot Manipulation

Understand

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 each subtask into moving and interaction phases, enabling the model to focus on phase-relevant sensory cues and enhancing subtask compatibility.