Rapid prototyping for series of tasks in atypical environment: robotic system with reliable program-based and flexible learning-based approaches

We propose a novel robotic system that combines both a reliable programming-based approach and a highly generalizable learning-based approach. How to design and implement a series of tasks in an atypical environment is a challenging issue. If all tasks are implemented using a programming-based approach, the development costs will be huge. However, if a learning-based approach is used, reliability is an issue. In this paper, we propose novel design guidelines that focus on the respective advantages of programming-based and learning-based approaches and select them so that they complement each other. We use a program-based approach for motions that is rough behavior and a learning-based approach for motion that is required complex interaction between robot and object of robot tasks and are difficult to achieve with a program. Our learning approach can easily and rapidly accomplish a series of tasks consisting of various motions because it does not require a computational model of an object to be designed in advance. We demonstrate a series of tasks in which randomly arranged parts are assembled using an actual robot.