Title: Precise but Natural Specification for Robot Tasks,
Abstract: We present Flipper, a natural language interface for describing high-level
task specifications for robots that are compiled into robot actions. Flipper
starts with a formal core language for task planning that allows expressing
rich temporal specifications and uses a semantic parser to provide a natural
language interface. Flipper provides immediate visual feedback by executing an
automatically constructed plan of the task in a graphical user interface. This
allows the user to resolve potentially ambiguous interpretations. Flipper
extends itself via naturalization: its users can add definitions for
utterances, from which Flipper induces new rules and adds them to the core
language, gradually growing a more and more natural task specification
language. Flipper improves the naturalization by generalizing the definition
provided by users. Unlike other task-specification systems, Flipper enables
natural language interactions while maintaining the expressive power and formal
precision of a programming language. We show through an initial user study that
natural language interactions and generalization can considerably ease the
description of tasks. Moreover, over time, users employ more and more concepts
outside of the initial core language. Such extensions are available to the
Flipper community, and users can use concepts that others have defined.