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arXiv · 1902.02870

Visual search and recognition for robot task execution and monitoring

Lorenzo Mauro·Francesco Puja·Simone Grazioso·Valsamis Ntouskos·Marta Sanzari·Edoardo Alati·Fiora Pirri
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Abstract

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.

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Lorenzo Mauro, Francesco Puja, Simone Grazioso, Valsamis Ntouskos, Marta Sanzari, Edoardo Alati, Fiora Pirri. 2019-02-07. Visual search and recognition for robot task execution and monitoring. https://doi.org/10.3233/978-1-61499-929-4-94

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