发表机构
Warsaw University of Technology; SANO Centre for Computational Personalized Medicine（华沙理工大学; SANO计算个性化医学中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文设计了一款结合上下文动作识别、行为树与ROS的主动式人机协作机器人系统，以Spot机器人为平台，通过上下文动作识别解决现有局限，提升复杂场景下的主动协助决策能力。
AI 中文摘要
本文提出一种主动式人机协作机器人系统的概念设计，该系统可识别人类活动并主动响应。系统利用上下文人类动作识别（HAR）来解读不同场景下的人类动作，同时采用行为树（BTs）定义动态且可解释的机器人行为。我们概述了系统架构，整合了上下文人类动作识别（HAR）、行为树（BTs）与ROS，并以Spot机器人平台作为代表性示例。我们解释HAR如何使机器人提供主动协助，探讨其局限性，并引入上下文HAR的方法以解决这些局限性，从而提升机器人在复杂人类活动场景中的决策能力。
英文摘要
This paper presents a conceptual design for a proactive human assisting robot system capable of recognizing human activities and responding proactively. The system leverages contextual human activity recognition to interpret human actions across diverse contexts, while behavior trees are utilized to define dynamic and interpretable robot behaviors. We outline the system architecture, incorporating contextual human action recognition (HAR), behavior trees (BTs), and ROS, using the Spot robot platform as a representative example. We explain how HAR enables the robot to provide proactive assistance, discuss its limitations, and introduce methodologies for contextual HAR to address these limitations, thereby enhancing the robot's decision-making in complex human activity scenarios.
CommentsPublished in Automation 2025: Recent Advances in Automation, Robotics and Measurement Techniques, Lecture Notes in Networks and Systems, vol. 1687, Springer Nature, 2025. DOI: 10.1007/978-3-032-08359-3_16
Journal refA. Ergogo and T. Zielińska, Automation 2025: Recent Advances in Automation, Robotics and Measurement Techniques, Lecture Notes in Networks and Systems, vol. 1687, Springer, 2026, pp. 175-186
DOI：10.1007/978-3-032-08359-3_16
