Word2vec到行为：形态学促进了机器语言的grounding
Word2vec to behavior: morphology facilitates the grounding of language in machines
摘要：
使机器能够对自然语言命令做出适当的反应，可以大大增加它们可以服务的人群。最近，神经网络训练的词嵌入的进步已经赋予了非实体文本处理算法以能力，并表明它们可以对实体机器具有类似的效用。在这里，我们介绍了一种方法，通过训练机器人采取类似的语义相似的word2vec编码命令。我们表明，这使他们能够采取适当的行动，训练后，以前闻所未闻的命令。最后，我们表明，诱导这样的对齐之间的运动和语言的相似性，可以促进或阻碍机器人的机械结构。这指向了未来的大规模方法，这些方法可以找到并利用动作、语言和机器人结构之间的关系。
英文摘要：
Enabling machines to respond appropriately to natural language commands could greatly expand the number of people to whom they could be of service. Recently, advances in neural network-trained word embeddings have empowered non-embodied text-processing algorithms, and suggest they could be of similar utility for embodied machines. Here we introduce a method that does so by training robots to act similarly to semantically-similar word2vec encoded commands. We show that this enables them to act appropriately, after training, to previously-unheard commands. Finally, we show that inducing such an alignment between motoric and linguistic similarities can be facilitated or hindered by the mechanical structure of the robot. This points to future, large scale methods that find and exploit relationships between action, language, and robot structure.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
