We introduce a novel paradigm for robot pro- gramming with which we aim to make robot programming more accessible for unexperienced users. In order to do so we incorporate two major components in one single framework: autonomous skill acquisition by robotic playing and visual programming. Simple robot program skeletons solving a task for one specific situation, so-called basic behaviours, are provided by the user. The robot then learns how to solve the same task in many different situations by autonomous playing which reduces the barrier for unexperienced robot programmers. Programmers can use a mix of visual programming and kinesthetic teaching in order to provide these simple program skeletons. The robot program can be implemented interactively by programming parts with visual programming and kinesthetic teaching. We further integrate work on experience-based skill-centric robot software testing which enables the user to continuously test implemented skills without having to deal with the details of specific components.
@cite_17: We introduce ROS Commander (ROSCo), an open source system that enables expert users to construct, share, and deploy robot behaviors for home robots. A user builds a behavior in the form of a Hierarchical Finite State Machine (HFSM) out of generic, parameterized building blocks, with a real robot in the develop and test loop. Once constructed, users save behaviors in an open format for direct use with robots, or for use as parts of new behaviors. When the system is deployed, a user can show the robot where to apply behaviors relative to fiducial markers (AR Tags), which allows the robot to quickly become operational in a new environment. We show evidence that the underlying state machine representation and current building blocks are capable of spanning a variety of desirable behaviors for home robots, such as opening a refrigerator door with two arms, flipping a light switch, unlocking a door, and handing an object to someone. Our experiments show that sensor-driven behaviors constructed with ROSCo can be executed in realistic home environments with success rates between 80 and 100 . We conclude by describing a test in the home of a person with quadriplegia, in which the person was able to automate parts of his home using previously-built behaviors.
@cite_17 follow a different approach with their system called (ROSCo). Generic parametrised skills are developed by experts in the respective field. End-users can combine them by visual programming in the form of hierarchical finite state machines (HFSM). These state machines are a generic representation of a skill which then can be deployed. Other users can load these state machines and adapt the skill to their local environment, e.g. by teaching where certain behaviours should be applied to. Our system uses a mixed paradigm in which the generic skills are either created by the end-user by visual programming and subsequent autonomous playing or by field experts which make the corresponding controller available.