Temporal Spatial Inverse Semantics for Robots Communicating with Humans
Temporal Spatial Inverse Semantics for Robots Communicating with Humans
复制标题
机器人与人类交流的时空逆语义
DOI：
10.1109/icra.2018.8460754
复制
发表时间：
2018
期刊：
影响因子：
--
通讯作者：
Yu Zhang
中科院分区：
文献类型：
--
作者：
Ze Gong;Yu Zhang
人类之间的有效沟通通常嵌入临时和空间上下文，而空间上下文则捕获了环境中对象的地理设置，临时上下文在本文中描述了它们的变化。语义方法还考虑与人类交流的机器人的临时环境。考虑到当前空间上下文与反向语义相比，人类的听众如何解释这些请求，我们的方法还通过参考过去的空间上下文来纳入临时上下文。生成的句子不仅可以参考基于延长句子结构的新度量的当前状态。单个句子是在不同时间指向环境状态的多个句子，我们能够生成句子，例如“请在餐桌上拿起杯子旁边的杯子”。随机生成实验域中的方案。使用空间上下文和我们的Amazon MTURK的逆语义。
Effective communication between humans often embeds both temporal and spatial context. While spatial context captures the geographic settings of objects in the environment, temporal context describes their changes over time. In this paper, we propose temporal spatial inverse semantics (TeSIS) to extend the inverse semantics approach to also consider the temporal context for robots communicating with humans. Inverse semantics generates natural language requests while taking into account how well the human listeners would interpret those requests given the current spatial context. Compared to inverse semantics, our approach incorporates also temporal context by referring to spatial context information in the past. To achieve this, we extend the sentence structure in inverse semantics to generate sentences that can refer to not only the current but also previous states of the environment. A new metric based on the extended sentence structure is developed by breaking a single sentence into multiple independent sentences that refer to environment states at different times. Using this approach, we are able to generate sentences such as “Please pick up the cup beside the oven that was on the dining table”. To evaluate our approach, we randomly generate scenarios in an experimental domain. Each scenario includes the description of the current and several immediate previous states. Natural language sentences are then generated for these scenarios using both inverse semantics that uses only the spatial context and our approach. Amazon MTurk is used to compare the sentences generated and results show that TeSIS achieves better accuracy, sometimes by a significant margin, than the baseline.