• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>The International journal of robotics research >Efficient grounding of abstract spatial concepts for natural language interaction with robot platforms
【24h】

Efficient grounding of abstract spatial concepts for natural language interaction with robot platforms

机译：有效的抽象空间概念基础，以实现自然语言与机器人平台的交互

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Our goal is to develop models that allow a robot to efficiently understand or "ground" natural language instructions in the context of its world representation. Contemporary approaches estimate correspondences between language instructions and possible groundings such as objects, regions, and goals for actions that the robot should execute. However, these approaches typically reason in relatively small domains and do not model abstract spatial concepts such as as "rows," "columns," or "groups" of objects and, hence, are unable to interpret an instruction such as "pick up the middle block in the row of five blocks." In this paper, we introduce two new models for efficient natural language understanding of robot instructions. The first model, which we call the adaptive distributed correspondence graph (ADCG), is a probabilistic model for interpreting abstract concepts that require hierarchical reasoning over constituent concrete entities as well as notions of cardinality and ordinality. Abstract grounding variables form a Markov boundary over concrete groundings, effectively de-correlating them from the remaining variables in the graph. This structure reduces the complexity of model training and inference. Inference in the model is posed as an approximate search procedure that orders factor computation such that the estimated probable concrete groundings focus the search for abstract concepts towards likely hypothesis, pruning away improbable portions of the exponentially large space of abstractions. Further, we address the issue of sealability to complex domains and introduce a hierarchical extension to a second model termed the hierarchical adaptive distributed correspondence graph (HADCG). The model utilizes the abstractions in the ADCG but infers a coarse symbolic structure from the utterance and the environment model and then performs fine-grained inference over the reduced graphical model, further improving the efficiency of inference. Empirical evaluation demonstrates accurate grounding of abstract concepts embedded in complex natural language instructions commanding a robotic torso and a mobile robot. Further, the proposed approximate inference method allows significant efficiency gains compared with the baseline, with minimal trade-off in accuracy.
机译：我们的目标是开发允许机器人在其世界表示形式的上下文中有效理解或“自然化”自然语言指令的模型。现代方法估计语言指令和可能的基础（例如对象，区域和机器人应执行的动作的目标）之间的对应关系。但是，这些方法通常会在相对较小的域中进行推理，并且不会对抽象空间概念（例如对象的“行”，“列”或“组”）进行建模，因此无法解释指令例如“在五个块的行中拾取中间的块。”在本文中，我们介绍了两个新模型，用于有效地理解机器人指令的自然语言。第一个模型，我们称为自适应分布式对应图（ADCG），是一种概率模型，用于解释抽象概念，这些抽象概念要求对组成的具体实体以及基数和序数的概念进行分层推理。抽象的接地变量在混凝土地面上形成一个马尔可夫边界，从而有效地将它们与图中的其余变量解相关。这种结构降低了模型训练和推理的复杂性。模型中的推论是一种近似的搜索过程，该过程对因子计算进行了排序，以使估计的可能的具体基础将对抽象概念的搜索集中在可能的假设上，从而修剪掉了指数级大空间中不可思议的部分。此外，我们解决了对复杂域的可密封性问题，并向称为分层自适应分布式对应图（HADCG）的第二个模型引入了分层扩展。该模型利用ADCG中的抽象，但从话语和环境模型中推断出粗略的符号结构，然后对简化的图形模型执行细粒度的推断，从而进一步提高了推断效率。实证评估证明了嵌入复杂自然语言指令中的抽象概念的准确基础，这些指令命令机器人躯干和移动机器人。此外，与基线相比，所提出的近似推理方法可以显着提高效率，并且在精度上的折衷最少。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号