Abstract
Typically, development of robot behavior entails writing the code, deploying it on a simulator or robot and running it in a test setting. If this feedback reveals errors, the programmer mentally needs to map the error in behavior back to the source code that caused it before being able to fix it. This process suffers from a long cognitive distance between the code and the resulting behavior, which slows down development and can make experimentation with different behaviors prohibitively expensive. In contrast, Live Programming tightens the feedback loop, minimizing the cognitive distance. As a result, programmers benefit from an immediate connection with the program that they are making thanks to an immediate, ‘live’ feedback on program behavior. This allows for extremely rapid creation, or variation, of robot behavior and for dramatically increased debugging speed. To enable such Live Robot Programming, in this article we discuss LRP; our language that provides for live programming of nested state machines. We detail the design of the language and show its features, give an overview of the interpreter and how it enables the liveness properties of the language, and illustrate its independence from specific robot APIs. • We implement a live programming language (LRP) for the behavioral layer of robots. • LRP is decoupled from specific robotics API. • We model the behavioral layer of robots with nested state machines. • Nested state machines are a robust paradigm in the context of live programming.
Published Version
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