PRF: A Program Reuse Framework for Automated Programming by Learning from Existing Robot Programs

This paper explores the problem of automated robot program generation from limited historical data when neither accurate geometric environmental models nor online vision feedback are available. The Program Reuse Framework (PRF) is developed, which uses expert-defined motion classes, a novel data str...

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Vydané v:	Robotics (Basel) Ročník 13; číslo 8; s. 118
Hlavní autori:	Toner, Tyler, Tilbury, Dawn M., Barton, Kira
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Basel MDPI AG 01.08.2024
Predmet:	automated programming Automation Data structures Datasets Deep learning Design End effectors Environment models Historical structures learning from demonstration (LfD) Libraries Manipulators Python Redundancy redundant manipulators Robot arms Robot dynamics Robots Sensors skill-based programming Skills task and motion planning workspace learning
ISSN:	2218-6581, 2218-6581
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Abstract	This paper explores the problem of automated robot program generation from limited historical data when neither accurate geometric environmental models nor online vision feedback are available. The Program Reuse Framework (PRF) is developed, which uses expert-defined motion classes, a novel data structure introduced in this work, to learn affordances, workspaces, and skills from historical data. Historical data comprise raw robot joint trajectories and descriptions of the robot task being completed. Given new tasks, motion classes are then used again to formulate an optimization problem capable of generating new open-loop, skill-based programs to complete the tasks. To cope with a lack of geometric models, a technique to learn safe workspaces from demonstrations is developed, allowing the risk of new programs to be estimated before execution. A new learnable motion primitive for redundant manipulators is introduced, called a redundancy dynamical movement primitive, which enables new end-effector goals to be reached while mimicking the whole-arm behavior of a demonstration. A mobile manipulator part transportation task is used throughout to illustrate each step of the framework.
AbstractList	This paper explores the problem of automated robot program generation from limited historical data when neither accurate geometric environmental models nor online vision feedback are available. The Program Reuse Framework (PRF) is developed, which uses expert-defined motion classes, a novel data structure introduced in this work, to learn affordances, workspaces, and skills from historical data. Historical data comprise raw robot joint trajectories and descriptions of the robot task being completed. Given new tasks, motion classes are then used again to formulate an optimization problem capable of generating new open-loop, skill-based programs to complete the tasks. To cope with a lack of geometric models, a technique to learn safe workspaces from demonstrations is developed, allowing the risk of new programs to be estimated before execution. A new learnable motion primitive for redundant manipulators is introduced, called a redundancy dynamical movement primitive, which enables new end-effector goals to be reached while mimicking the whole-arm behavior of a demonstration. A mobile manipulator part transportation task is used throughout to illustrate each step of the framework.
Author	Tilbury, Dawn M. Toner, Tyler Barton, Kira
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SubjectTerms	automated programming Automation Data structures Datasets Deep learning Design End effectors Environment models Historical structures learning from demonstration (LfD) Libraries Manipulators Python Redundancy redundant manipulators Robot arms Robot dynamics Robots Sensors skill-based programming Skills task and motion planning workspace learning
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Title	PRF: A Program Reuse Framework for Automated Programming by Learning from Existing Robot Programs
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