Vyas, AbhijitAbhijitVyasBeßler, DanielDanielBeßlerBeetz, MichaelMichaelBeetz2025-08-222025-08-222021https://media.suub.uni-bremen.de/handle/elib/22263https://doi.org/10.26092/elib/4228Dispositional qualities are the characteristics of an object that are attributed to it’s properties, such as mass, color, shape, material, etc. Understanding how the design of an object affords such qualities is a crucial task in robotics. Such as a cup being functionally designed to hold or contain something, and structurally designed to be carried or grasped by its handle. Dispositions tend to be more independent of an environment than affordances, since they are related to fundamental characteristics of an object. Whereas, affordances define the action possibilities with the object in the given environment with an agent capable of manipulating them, such as a bottle of water affords drinking possibility to an adult but it is hard for an infant to open the bottle cap in order to drink from it. The topic of affordances is widely explored in the domain of robotics where it plays a vital role for basic object manipulation skills. In this paper, we present an approach for disposition learning about an object from it’s shape and material information provided by a physics engine. We postulate our hypothesis around the current state of the art game engines which have complex object rendering and modelling techniques. The modelling of shape and material information about the object can be harvested as a source of knowledge for the given object in the environment. An intelligent agent thus benefits from having prior information about such objects in the world.11enhttps://creativecommons.org/licenses/by/4.0/dispositions learningaffordancesontology populationautonomous robotics000 Informatik, Informationswissenschaft, allgemeine Werke::000 Informatik, Wissen, SystemeText::Konferenzveröffentlichung::Tagungsband::Konferenzbeitrag10.26092/elib/4228urn:nbn:de:gbv:46-elib222636