Embodied Cognitive Robotics focuses its attention on the design of artificial agents capable of performing cognitive tasks autonomously. A central issue in this consists in studying process by which agents learn through interaction with their environment. Embodied Cognitive Robotics aims to implement models of cognitive processes coming from Cognitive Sciences. The guidelines in this research area are a direct response to the shortcomings of Classical Artificial Intelligence, where high-level tasks and behaviors were studied. This article describes the work carried out in the Cognitive Robotics Laboratory at the Universidad Autónoma del Estado de Morelos (UAEM). Our work is based on the concept of low-level sensorimotor schemes coded by Internal Models, thus falling as a matter of course within the tenets of Embodied Cognition, particularly with the idea that cognition must be understood as occurring in agents that have a body with which they interact in a specific environment. It is through this interaction that learning emerges laying the ground for cognitive processes. Our research includes theoretical work laying the foundations of Embodied Cognitive Robotics, as well as work with artificial and with natural agents.

Distance perception for mobile agents is of great importance for safe navigation in unknown environments. Traditional methods make use of analytical solutions. Yet, according to some research hypothesis, distance perception is not the result of mathematical calculations, but an emergent consequence of an association process, where visual and tactile information acquire a central role. Designing models closer to natural cognition poses paramount challenges to artificial intelligence (AI), which call for a review of some of the foundations of current methods. Our work is framed in the embodied cognition paradigm, which highlights the importance of the body for the development of cognitive processes. We provide theoretical grounds and empirical evidence for an artificial account of distance perception through a multimodal association process. By learning multimodal sensorimotor schemes, an agent is capable of perceiving affordances related to distance perception without any non-body-based geometric knowledge. We let an agent interact with an environment cluttered with objects, while learning multimodal sensorimotor associations. The learned spatial relations are thoroughly characterized to show how the model depends on the agent’s specific sensorimotor capabilities. The system is tested in a passability experiment and a navigation task, showing the agent anticipates undesired situations using the learned model predictions.

The emergence of altruistic behaviors in heterogeneous populations of autonomous robots, especially in signaling tasks, has proven to be a difficult problem to solve. However signaling and altruistic behaviors are present throughout the tree of life. Specially giving that, signaling behaviors seem to have evolved multiple times whenever there is a channel to emit a signal and one to receive it. In this work, this problem is addressed, using evolutionary algorithms, and modeling phenomena such as kin selection and kin discrimination in a biologically plausible way. We also used self-organizing maps to analyze the behavior of these populations during the evolutionary process, within the solution space. We believe that this approach can shed light on the predictive power of the Hamilton rule, the importance of kin selection in the evolution of altruistic behaviors, and how self-organizing maps can allow us to observe the different solutions in which the evolutionary algorithm converges through time.