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arXiv · 2504.07597

Learning Long Short-Term Intention within Human Daily Behaviors

Zhe Sun·Rujie Wu·Xiaodong Yang·Hongzhao Xie·Haiyan Jiang·Junda Bi·Zhenliang Zhang
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Abstract

In the domain of autonomous household robots, it is of utmost importance for robots to understand human behaviors and provide appropriate services. This requires the robots to possess the capability to analyze complex human behaviors and predict the true intentions of humans. Traditionally, humans are perceived as flawless, with their decisions acting as the standards that robots should strive to align with. However, this raises a pertinent question: What if humans make mistakes? In this research, we present a unique task, termed "long short-term intention prediction". This task requires robots can predict the long-term intention of humans, which aligns with human values, and the short term intention of humans, which reflects the immediate action intention. Meanwhile, the robots need to detect the potential non-consistency between the short-term and long-term intentions, and provide necessary warnings and suggestions. To facilitate this task, we propose a long short-term intention model to represent the complex intention states, and build a dataset to train this intention model. Then we propose a two-stage method to integrate the intention model for robots: i) predicting human intentions of both value-based long-term intentions and action-based short-term intentions; and 2) analyzing the consistency between the long-term and short-term intentions. Experimental results indicate that the proposed long short-term intention model can assist robots in comprehending human behavioral patterns over both long-term and short-term durations, which helps determine the consistency between long-term and short-term intentions of humans.

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Zhe Sun, Rujie Wu, Xiaodong Yang, Hongzhao Xie, Haiyan Jiang, Junda Bi, Zhenliang Zhang. 2025-04-10. Learning Long Short-Term Intention within Human Daily Behaviors. https://arxiv.org/abs/2504.07597

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