—The Linear Temporal Logic MissiOn Planning (LTLMoP) toolkit is a software package designed to assist in the rapid development, implementation, and testing of high-level robot controllers. In this toolkit, structured English and Linear Temporal Logic are used to write high-level reactive task specifications, which are then automatically transformed into… (More)
—This paper addresses the challenge of enabling non-expert users to command robots to perform complex high-level tasks using natural language. It describes an integrated system that combines the power of formal methods with the accessibility of natural language, providing correct-by-construction controllers for high-level specifications that can be… (More)
This paper presents an integrated system for generating, troubleshooting, and executing correct-by-construction controllers for autonomous robots using natural language input, allowing non-expert users to command robots to perform high-level tasks. This system unites the power of formal methods with the accessibility of natural language, providing… (More)
—The use of formal methods for synthesis has recently enabled the automated construction of verifiable high-level robot control. Most approaches use a discrete abstraction of the underlying continuous domain, and make assumptions about the physical execution of actions given a discrete implementation; examples include when actions will complete relative to… (More)
— This paper addresses the challenge of creating correct-by-construction controllers for robots whose actions are of varying execution durations. Recently, Linear Temporal Logic synthesis has been used to construct robot controllers for performing high-level tasks. During continuous execution of these controllers by a physical robot, one or more low-level… (More)
This paper shows an example application of the LTLMoP mission planning toolkit, in which an Aldebaran Nao and an iRobot Create each play a very basic game of hide-and-seek.
This video shows a demonstration of a fully autonomous robot, an iRobot ATRV-JR, which can be given commands using natural language. Users type commands to the robot on a tablet computer, which are then parsed and processed using semantic analysis. This information is used to build a plan representing the high level autonomous behaviors the robot should… (More)