Improving the effectiveness of human-robot interaction requires social robots to accurately infer human goals through robust intention understanding. This challenge is particularly critical in multimodal settings, where agents must integrate heterogeneous signals including text, visual cues to form a coherent interpretation of user intent. This paper presents IntentVLM, a novel two-stage video-language framework designed for open-vocabulary human intention recognition. The approach is inspired by forward-inverse modeling in cognitive science by decomposing intention understanding into goal candidate generation followed by structured inference through selection, effectively reducing hallucinations in latent reasoning. Evaluated on the IntentQA and Inst-IT Bench datasets, IntentVLM achieves state-of-the-art results with up to 80% accuracy, notably surpassing the baseline performance by 30% and matches human performance. Our findings demonstrate that this structured reasoning approach enhances open-vocabulary intention understanding without catastrophic forgetting, offering a robust foundation for human-centered robotics.

翻译：提升人机交互效能需要社交机器人通过稳健的意图理解准确推断人类目标。这一挑战在多模态环境中尤为关键，此时智能体必须整合文本、视觉线索等异质信号，形成对用户意图的一致解读。本文提出IntentVLM——一种面向开放词汇人类意图识别的新型两阶段视频-语言框架。该方法受认知科学中前向-逆向建模启发，将意图理解分解为目标候选生成与后续结构化选择推理两个阶段，有效减少潜在推理中的幻觉现象。在IntentQA和Inst-IT Bench数据集上的评估表明，IntentVLM取得了高达80%准确率的先进成果，尤其超越基线性能30%并达到人类水平。我们的研究发现表明，这种结构化推理方法在不发生灾难性遗忘的前提下增强了开放词汇意图理解能力，为人本机器人学奠定了坚实基础。