# Deep execution monitor for robot assistive tasks

arXiv: https://arxiv.org/abs/1902.02877 · HTML: https://mlsanity.com/paper/1902.02877/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Feb 2019.

## Built on by (most cited first, 1 of 1)

- [Using Visual Anomaly Detection for Task Execution Monitoring](https://mlsanity.com/paper/2107.14206/) (2021)

## Abstract (arXiv)

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.