让机器人看懂人在做什么，并主动帮忙。
Design of a Human-Assistance Robot System with Contextual Action Recognition
- 用上下文感知的活动识别理解人类行为
- 结合行为树实现可解释的机器人响应
- 适用于需要主动协作的智能助手机器人
本文提出一种主动式人机协作机器人系统的概念设计，能够识别人类活动并主动响应。系统通过上下文感知的人类活动识别（HAR）技术，在不同场景下解析人类动作，同时采用行为树（BTs）定义动态且可解释的机器人行为。系统架构整合了上下文HAR、行为树与ROS，以Spot机器人平台为例进行说明。文中阐述了HAR如何使机器人实现主动辅助，分析其局限性，并提出上下文感知HAR方法以克服这些限制，从而提升机器人在复杂人类活动场景下的决策能力。
原文摘要 · Abstract (English)
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.
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