Towards Long-Horizon Vision-Language-Action System: Reasoning, Acting and Memory

Vision-Language-Action (VLA) is crucial for autonomous decision-making in embodied systems. While current methods have advanced single-skill abilities, their short-horizon capability limits applicability in real-world scenarios. To address this challenge, we innovatively propose MindExplore, a general hierarchical VLA system with cross-skill for long-horizon tasks in highly dynamic sand. The key insight is to iteratively align the knowledge domain of task planning and action execution. Thus, this task-oriented action enables outstanding generalization across a wide range of real-world scenarios. In the reasoning layer, task-specific chains of thought (CoT) are designed for planning longhorizon task sequences and providing meta-action signals. In the acting layer, a simple but powerful Mixture of Pol- * Equal contribution. †