NRI-Small: A Biologically Plausible Architecture for Robotic Vision

这项研究的目标是开发一种通用的机器人视觉体系结构，该体系结构在生物上是可信的，并且在决策理论意义上是联合最优的，用于静态和动态环境中的注意力、对象跟踪、对象识别和动作识别。这项研究的动机是观察到，所有这些问题都是通过生物视觉和非常均匀的神经计算来解决的。这种方法是利用被接受的视觉皮质计算模型到统计学习和推理的基本计算中的映射，以便为所有任务得出统一的算法。智能优点：提出的视觉任务统一对机器人学来说是新颖的，也是至关重要的，因为机器人在计算上不可能实现大量的分离视觉算法。它还将利用任务协同效应，产生利用一个任务的解决方案来提高另一个任务的性能的算法。这可能会使视觉系统的整体性能更好。最后，该项目将通过引入自然图像统计的新模型，对视觉世界的结构以及机器人视觉如何利用它提出新的见解。更广泛的影响：该研究在制造业、智能系统、医疗保健、国土安全等领域具有适用性。预期的理论见解可能在统计学(特征依赖模型)、神经科学(神经计算模型)和计算机视觉(协同模型)中广泛应用。在教育方面，该项目为本科生参与研究提供了一个令人兴奋的机会。

The objective of this research is to develop a generic robotic vision architecture that is both biologically plausible and jointly optimal, in a decision theoretic sense, for attention, object tracking, object recognition, and action recognition, in both static and dynamic environments. The research is motivated by the observation that all these problems are solved by biological vision with very homogeneous neural computations. The approach is to exploit a mapping of accepted computational models of visual cortex into the elementary computations of statistical learning and inference in order to derive unified algorithms for all tasks. Intellectual merit: the proposed unification of vision tasks is novel and of paramount importance for robotics, since it is computationally infeasible for a robot to implement a large set of disjoint vision algorithms. It will also exploit task synergies, producing algorithms that leverage the solution of one task to improve performance on another. This will likely enable overall better performance of vision systems. Finally, the project will produce novel insights on the structure of the visual world, and how it can be leveraged by robotic vision, by introducing new models for natural image statistics. Broader impacts: The research has applicability in manufacturing, intelligent systems, health care, homeland security, etc. The expected theoretical insights are likely to be of wide application in statistics (models of feature dependence), neuroscience (models of neural computation), and computer vision (synergistic models). Educationally, the project provides an exciting opportunity for the involvement of undergraduates in research.