Deep execution monitor for robot assistive tasks
Alcor Lab, Diag, University of Rome La Sapienza（罗马一大诊断与人工智能实验室（Alcor Lab, Diag, University of Rome La Sapienza））
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
