让机器人在感知不准时仍能准确识别人类意图。
Uncertainty-Resilient Active Intention Recognition for Robotic Assistants
- 基于部分可观马尔可夫决策过程，融合实时传感器数据与规划器。
- 在真实机器人上验证，有效应对感知噪声与意图不确定性。
- 适合需要高可靠性的辅助机器人场景，如医疗陪护。
机器人助手的自主行为依赖于多个组件的协同。传统方法通常仅识别明确指令，限制了自主性，或假设信息近乎完美，导致过度简化。我们指出关键空白：人类意图识别中固有的结果不确定性和感知误差。为此，提出一种对不确定性与传感器噪声具有鲁棒性的框架，结合实时传感数据与多种规划器。核心为意图识别的POMDP模型，支持在不确定性下进行协作规划与执行。该集成框架已在物理机器人上成功测试，表现良好。
原文摘要 · Abstract (English)
Purposeful behavior in robotic assistants requires the integration of multiple components and technological advances. Often, the problem is reduced to recognizing explicit prompts, which limits autonomy, or is oversimplified through assumptions such as near-perfect information. We argue that a critical gap remains unaddressed -- specifically, the challenge of reasoning about the uncertain outcomes and perception errors inherent to human intention recognition. In response, we present a framework designed to be resilient to uncertainty and sensor noise, integrating real-time sensor data with a combination of planners. Centered around an intention-recognition POMDP, our approach addresses cooperative planning and acting under uncertainty. Our integrated framework has been successfully tested on a physical robot with promising results.
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