This paper presents a framework to define a task with freedom and variability in its goal state. A robot could use this to observe the execution of a task and target a different goal from the observed one; a goal that is still compatible with the task description but would be easier for the robot to execute. We define the model of an environment state and an environment variation, and present experiments on how to interactively create the variation from a single task demonstration and how to use this variation to create an execution plan for bringing any environment into the goal state.
View on arXiv@article{costinescu2025_2502.03918,
title={ Adaptation of Task Goal States from Prior Knowledge },
author={ Andrei Costinescu and Darius Burschka },
journal={arXiv preprint arXiv:2502.03918},
year={ 2025 }
}