Multimodal language grounding for improved human-robot collaboration: exploring spatial semantic representations in the shared space of attention
Multimodal language grounding for improved human-robot collaboration: exploring spatial semantic representations in the shared space of attention
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改善人机协作的多模态语言基础：探索共享注意力空间中的空间语义表示
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发表时间：
2017
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通讯作者：
Dimosthenis Kontogiorgos
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作者：
Dimosthenis Kontogiorgos
人们对围绕我们并代表我们做出决定的人工智能技术越来越感兴趣。这就需要这种技术能够与人类沟通，理解自然语言和非语言行为，这些行为可能携带有关我们复杂物理世界的信息。今天的人工智能体仍然对我们周围的物理空间以及我们关注的对象或概念知之甚少。我们仍然缺乏计算方法来理解涉及我们周围物体和位置的人类对话的上下文。我们能否将人类对真实的世界感知的多模态线索作为机器人语言学习的一个例子？人工智能体和机器人能否通过观察人类如何与物理世界互动，以及他们如何在对话中提及和参与来了解物理世界？这个博士项目的重点是结合口头语言和多方对话提取的非言语行为，以提高人工智能体的上下文意识和空间理解。
There is an increased interest in artificially intelligent technology that surrounds us and takes decisions on our behalf. This creates the need for such technology to be able to communicate with humans and understand natural language and non-verbal behaviour that may carry information about our complex physical world. Artificial agents today still have little knowledge about the physical space that surrounds us and about the objects or concepts within our attention. We are still lacking computational methods in understanding the context of human conversation that involves objects and locations around us. Can we use multimodal cues from human perception of the real world as an example of language learning for robots? Can artificial agents and robots learn about the physical world by observing how humans interact with it and how they refer to it and attend during their conversations? This PhD project’s focus is on combining spoken language and non-verbal behaviour extracted by multi-party dialogue in order to increase context awareness and spatial understanding for artificial agents.