14 research outputs found
Discovering Communication
What kind of motivation drives child language development? This
article presents a computational model and a robotic experiment to articulate
the hypothesis that children discover communication as a result
of exploring and playing with their environment. The considered
robotic agent is intrinsically motivated towards situations in which
it optimally progresses in learning. To experience optimal learning
progress, it must avoid situations already familiar but also situations
where nothing can be learnt. The robot is placed in an environment in
which both communicating and non-communicating objects are present.
As a consequence of its intrinsic motivation, the robot explores this environment
in an organized manner focusing first on non-communicative
activities and then discovering the learning potential of certain types of
interactive behaviour. In this experiment, the agent ends up being interested
by communication through vocal interactions without having
a specific drive for communication
Learning motor dependent CrutchïŹeld's information distance to anticipate changes in the topology of sensory body maps
International audienceWhat can a robot learn about the structure of its own body when he does not already know the semantics, the type and the position of its sensors and motors? Previous work has shown that an information theoretic approach, based on pairwise CrutchïŹeld's information distance on sensorimotor channels, could allow to measure the informational topology of the set of sensors, i.e. reconstruct approximately the topology of the sensory body map. In this paper, we argue that the informational sensors topology changes with motor conïŹgurations in many robotic bodies, but yet, because measuring CrutchïŹeld's distance is very time consuming, it is impossible to remeasure the body's topology for each novel motor conïŹguration. Rather, a model should be learnt that allows the robot to predict CrutchïŹeld's informational distances, and thus anticipate informational body maps, for novel motor conïŹgurations. We present experiments showing that learning motor dependent CrutchïŹeld distances can indeed be achieved
Emergence of Organisms.
Since early cybernetics studies by Wiener, Pask, and Ashby, the properties of living systems are subject to deep investigations. The goals of this endeavour are both understanding and building: abstract models and general principles are sought for describing organisms, their dynamics and their ability to produce adaptive behavior. This research has achieved prominent results in fields such as artificial intelligence and artificial life. For example, today we have robots capable of exploring hostile environments with high level of self-sufficiency, planning capabilities and able to learn. Nevertheless, the discrepancy between the emergence and evolution of life and artificial systems is still huge. In this paper, we identify the fundamental elements that characterize the evolution of the biosphere and open-ended evolution, and we illustrate their implications for the evolution of artificial systems. Subsequently, we discuss the most relevant issues and questions that this viewpoint poses both for biological and artificial systems
Symmetry: a basis for sensorimotor reconstruction
technical reportGiven a set of unknown sensors and actuators, sensorimotor reconstruction is achieved by exploiting relations between the sensor data and the actuator control data to determine sets of similar sensors, sets of similar actuators, necessary relations between them, as well as sensorimotor relations to the environment. Several Author's have addressed this problem, and we propose here a principled approach that exploits various symmetries and that achieves more efficient and robust results. A theoretical position is defined, the approach shown more efficient than previous work, and experimental results given
Using Strategic Movement to Calibrate a Neural Compass: A Spiking Network for Tracking Head Direction in Rats and Robots
The head direction (HD) system in mammals contains neurons that fire to represent the direction the animal is facing in its environment. The ability of these cells to reliably track head direction even after the removal of external sensory cues implies that the HD system is calibrated to function effectively using just internal (proprioceptive and vestibular) inputs. Rat pups and other infant mammals display stereotypical warm-up movements prior to locomotion in novel environments, and similar warm-up movements are seen in adult mammals with certain brain lesion-induced motor impairments. In this study we propose that synaptic learning mechanisms, in conjunction with appropriate movement strategies based on warm-up movements, can calibrate the HD system so that it functions effectively even in darkness. To examine the link between physical embodiment and neural control, and to determine that the system is robust to real-world phenomena, we implemented the synaptic mechanisms in a spiking neural network and tested it on a mobile robot platform. Results show that the combination of the synaptic learning mechanisms and warm-up movements are able to reliably calibrate the HD system so that it accurately tracks real-world head direction, and that calibration breaks down in systematic ways if certain movements are omitted. This work confirms that targeted, embodied behaviour can be used to calibrate neural systems, demonstrates that âgroundingâ of modelled biological processes in the real world can reveal underlying functional principles (supporting the importance of robotics to biology), and proposes a functional role for stereotypical behaviours seen in infant mammals and those animals with certain motor deficits. We conjecture that these calibration principles may extend to the calibration of other neural systems involved in motion tracking and the representation of space, such as grid cells in entorhinal cortex
Grand Challenge 7: Journeys in Non-Classical Computation
We review progress in Grand Challenge 7 : Journeys in Non-Classical Computation. We overview GC7-related events, review some background work in certain aspects of GC7 (hypercomputation, bio-inspired computation, and embodied computation) and identify some of the unifying challenges. We review the progress in implementations of one class of non-classical computers: reaction-diffusion systems. We conclude with warnings about âregression to the classicalâ