Visual search of relevant targets in the environment is a crucial robot skill. We propose a preliminary framework for the execution monitor of a robot task, taking care of the robot attitude to visually searching the environment for targets involved in the task. Visual search is also relevant to recover from a failure. The framework exploits deep reinforcement learning to acquire a "common sense" scene structure and it takes advantage of a deep convolutional network to detect objects and relevant relations holding between them. The framework builds on these methods to introduce a vision-based execution monitoring, which uses classical planning as a backbone for task execution. Experiments show that with the proposed vision-based execution monitor the robot can complete simple tasks and can recover from failures in autonomy.
@article{arxiv.1902.02870,
title = {Visual search and recognition for robot task execution and monitoring},
author = {Lorenzo Mauro and Francesco Puja and Simone Grazioso and Valsamis Ntouskos and Marta Sanzari and Edoardo Alati and Fiora Pirri},
journal= {arXiv preprint arXiv:1902.02870},
year = {2019}
}