Robot Meets World - Ontologies for Robotic Task &
                                Motion Planning : Extended Abstract
                                Jona Thai1
                                1
                                    Semantic Technologies Lab, Department of Mechanical & Industrial Engineering, University of Toronto


                                                                         Abstract
                                                                         Despite advancements in machine learning and robotics techniques, there still exists a wide range of
                                                                         activities that robots are ill-equipped to perform. This is known as the tail end of edge cases - novel
                                                                         scenarios that The goal of this research project is to identify, design and integrate ontologies for robotic
                                                                         spatial reasoning for use in these unaddressed scenarios. For example, deducing the correct key from
                                                                         the shape of a keyhole, and or determining one’s relative size to a hole. The challenge lies in striking a
                                                                         balance between expressivity (the capacity for the ontology to support perception and reasoning) and
                                                                         complexity (a large set of arbitrary axioms can be difficult to verify and maintain over a period of time).
                                                                         We combat this by using a model-based approach, and designing a curated set of competency questions
                                                                         of increasing difficulty that should build upon a shared ontology signature. These ontologies will then be
                                                                         compiled and integrated into a functioning robotic system as a demo. In addition to being a data-efficient
                                                                         approach capable of solving problems that would be intractable via traditional methods, it is more easily
                                                                         regulated due to its explainable nature (there exists a logical train of thought between the ontology
                                                                         axioms and the final proof result).




                                1. Motivation
                                For roboticists, we provide a complementary method to machine learning and mechanics, for
                                robotic spatial reasoning. According to our hypothesis, our method would require significantly
                                less data while still being able to generalize to novel scenarios e.g. reasoning about complemen-
                                tary keyhole shapes, or navigating unfamiliar terrain.
                                For ontologists, a focus on robotics applications can serve as a new guide for practical, expressive,
                                first-order ontologies. In the past 20 years, we have seen widespread adoption of ontologies as
                                a tool for taxonomy and resolving data conflicts. However, we see the potential for ontologies
                                to play a bigger role in a reasoning capacity. Robotics is a good entry point due to its plethora
                                of existing unresolved problems, and relative ease of access to prototyping.




                                FOIS 2023 Early Career Symposium (ECS), held at FOIS 2023, co-located with 9th Joint Ontology Workshops (JOWO
                                2023), 19-20 July, 2023, Sherbrooke, Québec, Canada
                                Envelope-Open jona.thai@mail.utoronto.ca (J. Thai)
                                                                       © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
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2. Research Questions

   1. What ontologies are needed for robots to replicate common-sense spatial reasoning?
   2. What are the minimum axioms needed to represent a physical space?


3. Objective(s)

   1. A curated set of spatiotemporal reasoning problems
   2. A set of ontologies representing mereotopologies in space
   3. A demo of ontologies working within a holistic robotic system


4. Research Methodology
We use competency questions to verify and validate our ontological choices,


5. Research Results to Date

   1. Preliminary set of robotics challenge problems [[1]]
   2. Preliminary example of robotic path finding problem modelled as a Process Specification
      Language (PSL) statement[2]
   3. Non-exhaustive ontological framework for robot anatomy, based on Hilbert’s Geometry
      and the Process Specification Language (PSL)[1][2]

References
[1] J. Thai, M. Grüninger, Robot meets world, in: Proceedings of the Joint Ontology Workshops co-located with the
    Bolzano Summer of Knowledge (BOSK 2020), IOS Press, 2020.
[2] J. Thai, M. Grüninger, Qualitative spatial ontologies for robot dynamics, in: Proceedings of the Joint Ontology
    Workshops, part of the Bolzano Summer of Knowledge (BOSK 2021), co-located with the 12th International
    Conference on Formal Ontologies in Information Systems (FOIS 2021) and the 12th International Conference on
    Biomedical Ontologies (ICBO 2021), IOS Press, 2021.