Catálogo de publicaciones - libros

We found that single neurons in the parietal area V6A of the macaque brain deal with all the components of reaching and grasping actions: locating in space the object target of action, directing the eyes toward it, sensing where the arm is in space, directing the arm toward the spatial location where the object is in order to reach and grasp it, adapting the grip to the object shape and size. The knowledge of how the brain codes simple visuomotor acts can be useful to build artificially-intelligent systems that have to interact with objects, localize them, direct their arm toward them, and grasp them with their gripper. Single cell recordings can also be useful in understanding how to perform more complex visuomotor tasks, like interacting with human beings, exchanging objects with them, and acting in an ever changing environment.

We are leaving in a world where the interaction with intelligent machines is an every day life event. The advances in artificial intelligence had allowed the development of adaptive machines that can modify its internal parameters to adjust their behavior according to the changing environment. One field that has profit from this is rehabilitation and prosthetics. In this respect, is our interest to evaluate the effects that this interaction has on the user. In this study, we use an f-MRI (functional Magnetic Resonance Imaging) device to measure the changes on the motor and sensory cortex of a right hand amputee’s using an EMG controlled Adaptable prosthetic hand with tactile feedback. Our results show the improvement in the adaptation to the prosthetic device, also, our experiments point to a possible modification of the body schema, generating an illusion of belonging of the robot hand to the human body.

Although the definition and measurement of intelligence is clearly of fundamental importance to the field of artificial intelligence, no general survey of definitions and tests of machine intelligence exists. Indeed few researchers are even aware of alternatives to the Turing test and its many derivatives. In this paper we fill this gap by providing a short survey of the many tests of machine intelligence that have been proposed.

Robots are rapidly evolving from factory work-horses to robot-companions. The future of robots, as our companions, is highly dependent on their abilities to understand, interpret and represent the environment in an efficient and consistent fashion, in a way that is compatible to humans. The work presented here is oriented in this direction. It suggests a hierarchical, concept oriented, probabilistic representation of space for mobile robots. A salient aspect of the proposed approach is that it is holistic - it attempts to create a consistent link from the sensory information the robot acquires to the human-compatible spatial concepts that the robot subsequently forms, while taking into account both uncertainty and incompleteness of perceived information. The approach is aimed at increasing spatial awareness in robots.

Empirical evidence indicates that grounded in the sensorimotor neural apparatus are crucially involved in several low and high level cognitive functions, including attention, motor control, planning, and goal-oriented behavior. A unitary theoretical framework is emerging that emphasizes how capabilities enable social abilities, too, including joint attention, imitation, perspective taking and communication. We argue that anticipation will be a key element for bootstrapping high level cognitive functions in cognitive robotics, too. We thus propose the challenge of understanding how anticipatory representations, that serve for and not only with the present, develop in situated agents.

Computers have become an essential part of modern life, providing services in a multiplicity of ways.  Access to these services, however, comes at a price: human attention is bound and directed toward a technical artifact in a human-machine interaction setting at the expense of time and attention for other humans. This paper explores a new class of computer services that support human- interaction and communication and . Computers in the Human Interaction Loop (CHIL), require consideration of all communication modalities, multimodal integration and more robust performance. We review the technologies and several CHIL services providing human-human support. Among them, we specifically highlight advanced computer services for communication.

The chapter discusses a recent paradigm shift in the field of Artificial Intelligence regarding the nature of human intelligence and its implications for the design and development of intelligent robots. It will be argued that social intelligence is not a mere ‘add-on’ to intelligent robot behaviour for the practical purpose of enabling the robot to interact smoothly with other robots or people, but that social intelligence might be a stepping stone towards more human-like, embodied artificial intelligence. The argument is supported by discussions in primatology highlighting the social origins of primate intelligence. The chapter also discusses challenges and opportunities provided by socially intelligent robots, with implications for our future.

Children seem intrinsically motivated to manipulate, to explore, to test, to learn and they look for activities and situations that provide such learning opportunities. Inspired by research in developmental psychology and neuroscience, some researchers have started to address the problem of designing intrinsic motivation systems. A robot controlled by such systems is able to autonomously explore its environment not to fulfil predefined tasks but driven by an incentive to search for situations where learning happens efficiently. In this paper, we present the origins of these intrinsically motivated machines, our own research in this novel field and we argue that intrinsic motivation might be a crucial step towards machines capable of life-long learning and open-ended development.

I recently gave a robot demonstration to a class of 1-grade elementary school children. In the school’s gymnasium hall, a few dozen 6-year-olds gathered enthusiastically around a few shiny machines with plenty of sensors and actuators, demonstrating patterns of locomotion. “These robots learned how to move by themselves” – I explained. “Some even developed their own shape”, I said, pointing at a set of 3D-printed plastic robots whose morphology and control evolved in simulation.

This paper focuses on designing a goal based rational component of a believable agent which has to interact with facial expressions with humans in communicative scenarios like teaching. One of the main concerns of the proposed model is to define interactions among rationality, personality and emotion in order to fulfill the idea of making rational decisions with emotional regulation. Our research aims are directed towards improving decision making process by means of applying Data Fusion techniques, especially Ordered Weighted Averaging (OWA) operator as a goal selection mechanism. Also the issue of obtaining weights for OWA aggregation is discussed. Finally the suggested algorithm is tested and results are provided with a real benchmark.