Towards a Knowledge Engineering Methodology for Flexible Robot Manipulation in Everyday Tasks

In the last decade, there have been great advancements in household robotics, enabling robots to autonomously accomplish household tasks. These robots are typically programmed for specific tasks and/ or objects. We hypothesise that the lack of flexibility in fulfilling new ad-hoc task requests can be overcome by a knowledge-based approach, allowing robots to infer how to address a new task or carry out known tasks on new objects.
Towards this goal, we propose a knowledge-based methodology that leverages knowledge already existing on the web to construct an ontology supporting robots in reasoning about parameters that influence manipulation actions for execution of task variations on a range of objects. The ontology comprises object and action
information, covering dispositions and affordances as well as task-specific properties. As a proof-of-concept, we manually construct a food-cutting ontology by importing and linking knowledge from relevant ontologies in addition to extracting and semantically enhancing knowledge from unstructured web sources. We demonstrate how robots can query the ontology and translate the contained information into action parameters. We evaluate the feasibility of the created ontology by simulating a robot accessing the ontology for parameterisation of actions to perform task variations of cutting.