Task-Oriented Robot Cognitive Manipulation Planning Using Affordance Segmentation and Logic Reasoning

The purpose of task-oriented robot cognitive manipulation planning is to enable robots to select appropriate actions to manipulate appropriate parts of an object according to different tasks, so as to complete the human-like task execution. This ability is crucial for robots to understand how to manipulate and grasp objects under given tasks. This article proposes a task-oriented robot cognitive manipulation planning method using affordance segmentation and logic reasoning, which can provide robots with semantic reasoning skills about the most appropriate parts of the object to be manipulated and oriented by tasks. Object affordance can be obtained by constructing a convolutional neural network based on the attention mechanism. In view of the diversity of service tasks and objects in service environments, object/task ontologies are constructed to realize the management of objects and tasks, and the object-task affordances are established through causal probability logic. On this basis, the Dempster-Shafer theory is used to design a robot cognitive manipulation planning framework, which can reason manipulation regions' configuration for the intended task. The experimental results demonstrate that our proposed method can effectively improve the cognitive manipulation ability of robots and make robots preform various tasks more intelligently.