Visual Object Affordances using Geometric Characteristics for Early Risk Detection

Since cohabitation with robots is transitioning from fiction to reality, ensuring their safe and efficient adoption is essential. However, a robot learning a new task needs to explore the environment, meaning that in certain situations potentially new unsafe actions might be attempted by a robotic agent. Therefore, it is imperative that they can anticipate latent dangers in object usage. A plausible approach for it is the use of object affordances. Affordances are the possibilities for action that an object or environment provides to a person or animal. This paper proposes a convolutional neural network-based affordance model to mitigate the risks posed by robotic agents when interacting with objects. The method uses geometric and 3D features to identify potential hazards such as cuts or impacts. The outcomes encompass the design of an architecture and focus on enhancing the safety of both the environment and those interacting with the robotic agent. The results obtained show that the model is able to identify efficiently the risk related to each object and even recognize the kind of hazards from unknown objects.