Ontological Component-based Description of Robot Capabilities

TL;DR

This paper proposes an ontological approach for robots to infer their capabilities from their components, enabling better self-awareness and interaction in collaborative tasks.

Contribution

It introduces a novel ontological method to infer robot capabilities from components and low-level functions, enhancing self-awareness and external interaction.

Findings

Capability inference from components is feasible.

The approach improves robot self-awareness.

Enhanced interaction with external entities.

Abstract

A key aspect of a robot's knowledge base is self-awareness about what it is capable of doing. It allows to define which tasks it can be assigned to and which it cannot. We will refer to this knowledge as the Capability concept. As capabilities stems from the components the robot owns, they can be linked together. In this work, we hypothesize that this concept can be inferred from the components rather than merely linked to them. Therefore, we introduce an ontological means of inferring the agent's capabilities based on the components it owns as well as low-level capabilities. This inference allows the agent to acknowledge what it is able to do in a responsive way and it is generalizable to external entities the agent can carry for example. To initiate an action, the robot needs to link its capabilities with external entities. To do so, it needs to infer affordance relations from its capabilities as well as the external entity's dispositions. This work is part of a broader effort to integrate social affordances into a Human-Robot collaboration context and is an extension of an already existing ontology.