用在线部分可观模型提升机器人对人类意图的实时识别能力。
Towards Intention Recognition for Robotic Assistants Through Online POMDP Planning
- 基于部分可观马尔可夫决策过程构建在线意图识别框架
- 在存在干扰和观测噪声的工业场景中实现有效意图推断
- 适合需要主动协作的机器人辅助系统研究者参考
意图识别，即预测另一智能体行为的能力，在设计支持人类日常任务的自动化助手时至关重要。尤其在工业场景中，决策者可能面临干扰，且观测信息存在噪声或不完整。在此背景下，需协助人类工人的机器人助手必须在主动获取信息与执行自身任务之间动态权衡，这一策略被称为主动目标识别。本文提出一种用于在线意图识别的部分可观模型，展示初步实验结果，并讨论该类问题中的若干挑战。
原文摘要 · Abstract (English)
Intention recognition, or the ability to anticipate the actions of another agent, plays a vital role in the design and development of automated assistants that can support humans in their daily tasks. In particular, industrial settings pose interesting challenges that include potential distractions for a decision-maker as well as noisy or incomplete observations. In such a setting, a robotic assistant tasked with helping and supporting a human worker must interleave information gathering actions with proactive tasks of its own, an approach that has been referred to as active goal recognition. In this paper we describe a partially observable model for online intention recognition, show some preliminary experimental results and discuss some of the challenges present in this family of problems.
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