Recently, artificial neural networks (ANNs) have been applied to various robot-related research areas due to their powerful spatial feature abstraction and temporal information prediction abilities. Decision-making has also played a fundamental role in the research area of robotics. How to improve ANNs with the characteristics of decision-making is a challenging research issue. ANNs are connectionist models, which means they are naturally weak in long-term planning, logical reasoning, and multistep decision-making. Considering that a small refinement of the inner network structures of ANNs will usually lead to exponentially growing data costs, an additional planning module seems necessary for the further improvement of ANNs, especially for small data learning. In this paper, we propose a state operator and result (SOAR) improved ANN (SANN) model, which takes advantage of both the long-term cognitive planning ability of SOAR and the powerful feature detection ability of ANNs. It mimics the cognitive mechanism of the human brain to improve the traditional ANN with an additional logical planning module. In addition, a data fusion module is constructed to combine the probability vector obtained by SOAR planning and the original data feature array. A data fusion module is constructed to convert the information from the logical sequences in SOAR to the probabilistic vector in ANNs. The proposed architecture is validated in two types of robot multistep decision-making experiments for a grasping task: a multiblock simulated experiment and a multicup experiment in a real scenario. The experimental results show the efficiency and high accuracy of our proposed architecture. The integration of SOAR and ANN is a good compromise between logical planning with small data and probabilistic classification with big data. It also has strong potential for more complicated tasks that require robust classification, long-term planning, and fast learning. Some potential applications include recognition of grasping order in multiobject environment and cooperative grasping of multiagents.

近年来，由于其强大的空间特征抽象和时间信息预测能力，人工神经网络（ANN）已应用于各种机器人相关的研究领域。决策在机器人技术的研究领域也发挥了重要作用。如何改进具有决策特征的人工神经网络是一个具有挑战性的研究问题。人工神经网络是连接主义模型，这意味着它们在长期计划，逻辑推理和多步骤决策中自然较弱。考虑到人工神经网络内部网络结构的细微改进通常会导致数据成本呈指数增长，因此似乎需要一个额外的计划模块来进一步改善人工神经网络，特别是对于小数据学习。在本文中，我们提出了状态算子和结果（SOAR）改进的ANN（SANN）模型，它充分利用了SOAR的长期认知计划能力和ANN强大的特征检测能力。它通过附加的逻辑计划模块模仿人脑的认知机制，以改善传统的人工神经网络。另外，构建数据融合模块以将通过SOAR规划获得的概率向量与原始数据特征数组进行组合。构建数据融合模块以将信息从SOAR中的逻辑序列转换为ANN中的概率向量。在两种类型的机器人多步决策实验中，针对一种抓紧任务验证了所提出的体系结构：多块模拟实验和真实场景中的多杯实验。实验结果表明我们提出的体系结构的效率和高精度。SOAR和ANN的集成是小数据逻辑规划和大数据概率分类之间的良好折衷。对于需要复杂分类，长期计划和快速学习的更复杂任务，它也具有强大的潜力。一些潜在的应用包括识别多对象环境中的抓握顺序和多主体的协同抓握。