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This paper describes a framework called MaestROB. It is designed to make therobots perform complex tasks with high precision by simple high-levelinstructions given by natural language or demonstration. To realize this, ithandles a hierarchical structure by using the knowledge stored in the forms ofontology 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 WatsonAPIs for cognitive capabilities and semantic understanding, and orchestrationof these components by a new open source robot middleware called Project Intuat its core. We show how this framework can be used in a complex scenario wheremultiple actors (human, a communication robot, and an industrial robot)collaborate to perform a common industrial task. Human teaches an assembly taskto Pepper (a humanoid robot from SoftBank Robotics) using natural languageconversation and demonstration. Our framework helps Pepper perceive the humandemonstration and generate a sequence of actions for UR5 (collaborative robotarm from Universal Robots), which ultimately performs the assembly (e.g.insertion) task.

MaestROB: A Robotics Framework for Integrated Orchestration of Low-Level Control and High-Level Reasoning