UNCOM：面向桌面上场景的零样本上下文感知命令理解
UNCOM: Zero-shot Context-Aware Command Understanding for Tabletop Scenarios
AI总结：
UNCOM提出了一种新的混合框架，通过整合语音、手势和场景上下文信息，实现桌面上场景中自然人类命令的解析，无需预定义对象模型或特定任务训练数据，展示了系统的鲁棒性。
摘要：
本文提出了UNCOM，一种新的混合框架，用于解读桌面上场景中的自然人类命令。系统整合了语音、手势和场景上下文等多种信息，以提取结构化、可操作的指令供机器人执行。为满足家庭环境中通用的人机交互需求，UNCOM设计为零样本操作，不依赖预定义的对象模型或特定任务的训练数据。使用基础和任务特定的深度学习模型，它允许即开即用的语音识别、自然语言理解、手势检测和物体分割。模块化架构通过显式解析命令为对象-动作-目标表示，增强了透明度和可解释性，使系统能够与符号机器人框架集成。我们在TIAGo++机器人上展示了该系统，并在真实世界的人机交互数据集上进行了评估；在我们的基准数据集上实现了82.39%的成功率，突显了系统对多样性、噪声和沟通模糊性的鲁棒性。数据集、评估场景和代码已公开，以支持未来研究。
英文摘要：
This paper presents UNCOM, a novel hybrid framework for interpreting natural human commands in tabletop scenarios. The system integrates multiple sources of information -- speech, gestures, and scene context -- to extract structured, actionable instructions for robots. Addressing the need for general-purpose human-robot interaction in domestic environments, UNCOM is designed for zero-shot operation, without reliance on predefined object models or training data specific to a given task. Using foundational and task-specific deep learning models, it allows out-of-the-box speech recognition, natural language understanding, gesture detection, and object segmentation. The modular architecture enhances transparency and explainability by explicitly parsing commands into object-action-target representations, enabling integration with symbolic robotic frameworks. We demonstrate the system in a TIAGo++ robot and provide an evaluation on a real-world data set of human-robot interaction scenarios; achieving an 82.39\% success rate over our benchmark data set, highlighting the robustness of the system to diversity, noise, and communication ambiguity. The data set, evaluation scenarios, and the code are publicly available to support future research.
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