Deep execution monitor for robot assistive tasks
This work addresses robot assistive tasks, such as in warehouses, but appears incremental as it applies deep learning to an existing execution monitoring problem.
The paper tackled the problem of predicting the next subtask in robot assistive tasks by introducing a deep model for sequencing goals and visually evaluating task state, showing that deep learning supports task-level planning and robot operations while coping with non-determinism, and it leveraged robot performance with measured improvements in warehouse helping tasks.
We consider a novel approach to high-level robot task execution for a robot assistive task. In this work we explore the problem of learning to predict the next subtask by introducing a deep model for both sequencing goals and for visually evaluating the state of a task. We show that deep learning for monitoring robot tasks execution very well supports the interconnection between task-level planning and robot operations. These solutions can also cope with the natural non-determinism of the execution monitor. We show that a deep execution monitor leverages robot performance. We measure the improvement taking into account some robot helping tasks performed at a warehouse.