Abstract
Recent technological advances have provided the manufacturing industry with precise and robust machines that perform better than their human counterparts in tiresome and tedious jobs. Likewise, robots can perform high precision tasks including in hazardous environments. However, a new area of research in robotics has emerged in the last decades, namely cognitive robotics. The main interest in this area is the study of cognitive processes in humans and their implementation and modeling in artificial agents. In cognitive robotics, the use of robots as platforms, in the study of cognition, is the best-suited mechanism as they naturally interact with their environment and learn through this interaction. Following these ideas, in these works, two low-level cognitive tasks are modeled and implemented in an artificial agent. Based on the ecological framework of perception, in the first experiment, an agent learns its body map. In the second experiment, the agent acquires a distance-to-obstacles concept. The agent is let to interact with its environment and allowed to build multimodal representations of its surroundings, known as affordances. Internal models are proposed as a conceptual mechanism which performs associations between different modalities. The results presented here provide the basis for further research on the capabilities of internal models as a constituent cognitive base for higher capabilities in artificial agents.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call