ZAS-F: A Zero-Shot Abstract Sub-Goal Framework Empowers Robots for Long Horizontal Inventory Tasks
Yongshuai Wu, Jian Zhang, Shaoen Wu, Shiwen Mao, Ying Wang · IEEE Access · 2025
Long-horizon tasks in unstructured environments are notoriously challenging for robots because they require the prediction of extensive action plans with thousands of steps while adapting to everchanging conditions by reasoning among multimodal sensing spaces. Humans can efficiently tackle such compound problems by breaking them down into easily reachable abstract sub-goals, significantly reducing complexity. Inspired by this ability, we explore how we can enable robots to acquire sub-goal formulation skills for long-horizon tasks and generalize them to novel situations and environments. To address these challenges, we propose the Zero-shot Abstract Sub-goal Framework (ZAS-F), which empowers robots to decompose overarching action plans into transferable abstract sub-goals, thereby providing zero-shot capability in newtask conditions. ZAS-F is an imitation-learning-based method that efficiently learns a task policy from a few demonstrations. The learned policy extracts abstract features from multimodal and extensive temporal observations and subsequently uses these features to predict task-agnostic sub-goals by reasoning about their latent relations. We evaluated ZAS-F in radio frequency identification (RFID) inventory tasks across various dynamic environments, a typical long-horizon task requiring robots to handle unpredictable conditions, including unseen objects and structural layouts. Our experiments demonstrated that ZAS-F achieves a learning efficiency 30 times higher than previous methods, requiring only 8k demonstrations. Compared to prior approaches, ZAS-F achieves a 98.3% scanning accuracy while significantly reducing the training data requirement. Further, ZAS-F demonstrated strong generalization, maintaining a scan success rate of 99.4% in real-world deployment without additional fine-tuning. In long-term operations spanning 100 rooms, ZAS-F maintained consistent performance compared to short-term tasks, highlighting its robustness against compounding errors. These results establish ZAS-F as an efficient and adaptable solution for longhorizon robotic tasks in unstructured environments.