让机器人根据环境自动调整行为，提升交互自然度。
Affecta-Context: The Context-Guided Behavior Adaptation Framework
- 用物理环境特征聚类，指导行为选择
- 72次交互训练后，能优化行为优先级
- 可迁移至未访问过的新环境，适合人机交互研究
本文提出Affecta-Context框架，用于社交机器人在人机交互中实现行为自适应。该框架通过分析物理环境特征对行为进行分类，并在交互中学习行为优先级，以匹配当前环境与用户偏好。实验在两个不同物理场景中，由6名参与者完成72次交互训练，验证了框架在未见环境中泛化行为匹配的能力。结果表明，机器人能根据环境变化自主调整行为策略，实现更自然的人机协作。
原文摘要 · Abstract (English)
This paper presents Affecta-context, a general framework to facilitate behavior adaptation for social robots. The framework uses information about the physical context to guide its behaviors in human-robot interactions. It consists of two parts: one that represents encountered contexts and one that learns to prioritize between behaviors through human-robot interactions. As physical contexts are encountered the framework clusters them by their measured physical properties. In each context, the framework learns to prioritize between behaviors to optimize the physical attributes of the robot's behavior in line with its current environment and the preferences of the users it interacts with. This paper illlustrates the abilities of the Affecta-context framework by enabling a robot to autonomously learn the prioritization of discrete behaviors. This was achieved by training across 72 interactions in two different physical contexts with 6 different human test participants. The paper demonstrates the trained Affecta-context framework by verifying the robot's ability to generalize over the input and to match its behaviors to a previously unvisited physical context.
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