MaestROB: A Robotics Framework for Integrated Orchestration of Low-Level Control and High-Level Reasoning
Asim Munawar, Giovanni De Magistris, Tu-Hoa Pham, Daiki Kimura,, Michiaki Tatsubori, Takao Moriyama, Ryuki Tachibana, Grady Booch

TL;DR
MaestROB is a comprehensive robotics framework that integrates low-level control and high-level reasoning, enabling robots to perform complex tasks through natural language or demonstration by hierarchical processing and semantic understanding.
Contribution
It introduces a hierarchical architecture with ontology and rules, and a new middleware Project Intu, to seamlessly coordinate perception, actuation, and cognitive components.
Findings
Successful demonstration with Pepper and UR5 robots in collaborative assembly tasks.
Framework enables natural language teaching and demonstration-based programming.
Effective integration of perception, planning, and actuation in complex scenarios.
Abstract
This paper describes a framework called MaestROB. It is designed to make the robots perform complex tasks with high precision by simple high-level instructions given by natural language or demonstration. To realize this, it handles a hierarchical structure by using the knowledge stored in the forms of ontology and rules for bridging among different levels of instructions. Accordingly, the framework has multiple layers of processing components; perception and actuation control at the low level, symbolic planner and Watson APIs for cognitive capabilities and semantic understanding, and orchestration of these components by a new open source robot middleware called Project Intu at its core. We show how this framework can be used in a complex scenario where multiple actors (human, a communication robot, and an industrial robot) collaborate to perform a common industrial task. Human teaches…
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