Robot Cognition Laboratory, Integrative Neuroscience, Stem Cell and Brain Research Institute, Institut National de la Santé et de la Recherche Médicale U846 Bron, France.
Front Neurorobot. 2010 Jun 3;4:8. doi: 10.3389/fnbot.2010.00008. eCollection 2010.
The current research extends our framework for embodied language and action comprehension to include a teleological representation that allows goal-based reasoning for novel actions. The objective of this work is to implement and demonstrate the advantages of a hybrid, embodied-teleological approach to action-language interaction, both from a theoretical perspective, and via results from human-robot interaction experiments with the iCub robot. We first demonstrate how a framework for embodied language comprehension allows the system to develop a baseline set of representations for processing goal-directed actions such as "take," "cover," and "give." Spoken language and visual perception are input modes for these representations, and the generation of spoken language is the output mode. Moving toward a teleological (goal-based reasoning) approach, a crucial component of the new system is the representation of the subcomponents of these actions, which includes relations between initial enabling states, and final resulting states for these actions. We demonstrate how grammatical categories including causal connectives (e.g., because, if-then) can allow spoken language to enrich the learned set of state-action-state (SAS) representations. We then examine how this enriched SAS inventory enhances the robot's ability to represent perceived actions in which the environment inhibits goal achievement. The paper addresses how language comes to reflect the structure of action, and how it can subsequently be used as an input and output vector for embodied and teleological aspects of action.
当前的研究将我们的具身语言和动作理解框架扩展到包括目的论表示，以允许对新动作进行基于目标的推理。这项工作的目的是实现和展示一种混合的、具身目的论的动作-语言交互方法的优势，从理论角度和通过与 iCub 机器人的人机交互实验的结果来展示。我们首先展示了具身语言理解框架如何允许系统为处理目标导向动作（如“拿”、“盖”和“给”）开发一组基本的表示。口语和视觉感知是这些表示的输入模式，口语生成是输出模式。朝着目的论（基于目标的推理）方法前进，新系统的一个关键组成部分是这些动作的子组件的表示，其中包括初始使能状态和这些动作的最终结果状态之间的关系。我们展示了包括因果连接词（例如“因为”、“如果-那么”）在内的语法类别如何允许口语丰富所学的状态-动作-状态（SAS）表示。然后，我们研究了这种丰富的 SAS 清单如何增强机器人在环境抑制目标实现的情况下代表感知动作的能力。本文探讨了语言如何反映动作的结构，以及它如何随后作为动作的具身和目的论方面的输入和输出向量。
