Towards a human-inspired control architecture for visually-guided action

尽管机器人系统越来越复杂，但人类在许多方面仍然优于机器人--尤其是在自然环境中行动的时候。拟议的项目旨在了解一套对这种互动至关重要的基本过程，并通过开发一个根据人类行为进行评估的计算机模型来检验这一理解。要建模的过程涉及到我们对复杂(多对象)环境中存在的与任务相关的对象形成动作的能力。提出的模型基于这样的前提，即成功的人类行动依赖于两种形式的信息：物体的抽象功能(这是可以切割的东西)和与动作相关联的物体的视觉属性(可以抓住手柄，但不能抓住刀片)。心理学证据还表明，特定任务的预期行为会影响人类注意力的导向。选择要对其采取行动的对象与选择要进行哪个行动并不是分开的。为了捕捉到这一点，对象的选择和动作的选择将被建模为交互过程。申请者之前的工作为项目提供了一个有希望的背景。特别是，已经产生了一个对象选择模型(萨伊姆模型)，该模型已成功地应用于大量心理数据。第二个模型模拟了如何将动作选择到单个对象(NAM模型)。在这个项目中，这些模型将被合并在一起，以模拟感知选择和行动之间的交互作用。该模型将使用类似神经的体系结构实现，并与人类数据进行比较测试。从长远来看，该模型将为在复杂环境中操作的机械臂提供实时控制的架构

Despite the increasing sophistication of robotic systems, humans remain superior to robots in an impressive number of ways - not least when it comes to acting in natural environments. The proposed project aims at understanding a set of basic processes that are crucial for such interactions, with this understanding tested through the development of a computer model that is evaluated relative to human behaviour. The process to be modelled concern our ability to shape an action to a task-relevant object present in a complex (multi-object) environment. The proposed model is based on the premise that successful human actions depend on two forms of information: the abstract function of the objects (this is something to cut with) and the visual properties of the objects that are associated with action (that the handle but not the blade can be grasped). Psychological evidence also indicates that the intended action for a given task can influence how human attention is directed. Selection of the object to act upon is not separated from the selection of which action to make. To capture this, the selection of objects and the selection of actions will be modelled as interactive process.Prior work by the applicant provides a promising backdrop to the project. In particular, a model of object selection has been generated (the SAIM model) which has been successfully applied to a large body of psychological data. A second model has simulated how actions are select to single objects (the NAM model). In this project the models will be merged together, to simulate interactions between perceptual selection and action. The model will be realised using a neural-like architecture, and tested in comparison to human data. In the longer term, the model will provide an architecture for real-time control of a robot arm operating in a complex environment