Programming by demonstration

In computer science, programming by demonstration (PbD) is an end-user development technique for teaching a computer or a robot new behaviors by demonstrating the task to transfer directly instead of programming it through machine commands.

The terms programming by example (PbE) and programming by demonstration (PbD) appeared in software development research as early as the mid 1980s to define a way to define a sequence of operations without having to learn a programming language. The usual distinction in literature between these terms is that in PbE the user gives a prototypical product of the computer execution, such as a row in the desired results of a query; while in PbD the user performs a sequence of actions that the computer must repeat, generalizing it to be used in different data sets.

These two terms were first undifferentiated, but PbE then tended to be mostly adopted by software development researchers while PbD tended to be adopted by robotics researchers. Today, PbE refers to an entirely different concept, supported by new programming languages that are similar to simulators. This framework can be contrasted with Bayesian Program Synthesis.

The PbD paradigm is first attractive to the robotics industry due to the costs involved in the development and maintenance of robot programs. In this field, the operator often has implicit knowledge on the task to achieve (he/she knows how to do it), but does not have usually the programming skills (or the time) required to reconfigure the robot. Demonstrating how to achieve the task through examples thus allows to learn the skill without explicitly programming each detail.

The first PbD strategies proposed in robotics were based on teach-in, guiding or play-back methods that consisted basically in moving the robot (through a dedicated interface or manually) through a set of relevant configurations that the robot should adopt sequentially (position, orientation, state of the gripper). The method was then progressively ameliorated by focusing principally on the teleoperation control and by using different interfaces such as vision.

However, these PbD methods still used direct repetition, which was useful in industry only when conceiving an assembly line using exactly the same product components. To apply this concept to products with different variants or to apply the programs to new robots, the generalization issue became a crucial point. To address this issue, the first attempts at generalizing the skill were mainly based on the help of the user through queries about the user's intentions. Then, different levels of abstractions were proposed to resolve the generalization issue, basically dichotomized in learning methods at a symbolic level or at a trajectory level.

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