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- Title
PRF: A Program Reuse Framework for Automated Programming by Learning from Existing Robot Programs.
- Authors
Toner, Tyler; Tilbury, Dawn M.; Barton, Kira
- 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.
- Subjects
DATA structures; GEOMETRIC modeling; ROBOTS; ROBOT programming
- Publication
Robotics, 2024, Vol 13, Issue 8, p118
- ISSN
2218-6581
- Publication type
Article
- DOI
10.3390/robotics13080118