Gesticulating with NAO: Real-time Context-Aware Co-Speech Gesture Generation for Human-Robot Interaction
Gesticulating with NAO: Real-time Context-Aware Co-Speech Gesture Generation for Human-Robot Interaction
复制标题
使用 NAO 进行手势：用于人机交互的实时上下文感知共同语音手势生成
DOI：
10.1145/3610661.3620664
复制
发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Nguyen T
中科院分区：
文献类型：
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作者：
Nguyen T
人类自然会通过面部、身体和声音表达产生非语言行为，以在互动过程中向互动伙伴发出信息、意图和感受。考虑到机器人正逐步从研究实验室进入人类环境，它们越来越需要能够发展类似的社交智能技能。因此，为机器人配备人类非语言沟通技能一直是一个活跃的研究领域，数据驱动的端到端学习方法已成为主导，提供了可扩展性和通用性。然而，大多数最近的作品只考虑一个单一的字符建模的个人内部动态不注意互动的合作伙伴的行为。我们的研究旨在解决差距在文献中引入一个生成框架，允许社会机器人产生共同的语音手势，以传达他们的语音在一个场景中的实时人机交互。值得注意的是，该系统还将从交互伙伴观察到的非语言信号视为用于产生机器人的交流手势的条件输入。
Humans naturally produce nonverbal behaviours via facial, body, and vocal expressions to signal their messages, intentions, and feelings to their interacting partners during interactions. Considering robots are progressively moving out from research laboratories into human environments, there is an increasing need for them to be able to develop similar social intelligence skills. Equipping robots with human nonverbal communication skills, therefore, has been an active research area, where data-driven, end-to-end learning approaches have become predominant, offering scalability and generalisability. However, most recent works only consider a single character for modelling intrapersonal dynamics without paying attention to the interacting partner’s behaviours. Our research aims to address the gap in the literature by introducing a generative framework allowing social robots to produce co-speech gestures to convey their speech in a scenario of real-time human-robot interaction. Notably, the system also considers non-verbal signals observed from the interacting partner as a conditional input for producing robots’ communicative gestures.
同意或不同意在二元交互过程中根据情感上下文线索生成身体手势
DOI：
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发表时间：
2022
期刊：
IEEE International Symposium on Robot and Human Interactive Communication
影响因子：
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作者：
Nguyen Tan Viet Tuyen;O. Çeliktutan
通讯作者：
O. Çeliktutan