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| Neurorobotics: an experimental science of embodiment |
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Kaplan, F. (2008) Neurorobotics: an experimental science of embodiment. Frontiers in Neuroscience 2,1 : 22-23 |
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View article on Frontiers in Neuroscience website
At the interface of neuroscience and robotics, neurorobotics is the
science and technology of embodied autonomous neural systems. Neural
systems include brain-inspired algorithms (e.g. connectionist networks,
artificial spiking neural nets), computational models of biological
neural networks (e.g. large-scale simulations of neural microcircuits)
and actual biological systems (e.g. in vivo and in vitro neural nets).
Such neural systems can be embodied in machines with mechanic,
pneumatic, electromagnetic or any other forms of physical or virtual
actuation. This includes robots, prosthetic, wearable systems, virtual
reality environments but at also, at smaller scale, micro-machines and,
at the larger scales, furniture and infrastructures.
Neurorobotics is at the confluence of different research trends that
emancipated from their original disciplines in the 1980s and 1990s:
artificial neural network and computational neuroscience models that
are neural systems but not embodied, embodied developmental robotic
systems that are embodied and autonomous but not brain-like, and
research studies in hybrid biological - artificial systems that are
embodied neural systems but with very little autonomy. In each of these
fields great progresses have been recently made. Most advanced
computing machineries permit now to simulate huge realistic neural
network. Promising experiments in epigenetic robotics move from
traditional learning of isolated tasks to open-ended developmental
trajectories. Pioneering research in hybrid systems permit to
investigate symbiotic biological-artificial systems that were
previously only in the realm of science fiction. Neurorobotics is the
natural meeting point of these converging trends.
The grand challenge of neurorobotics is to build a well-founded
experimental science of embodiment. Considered in isolation, neural
systems tend to be intrinsically generic, plastic and versatile.
However, once embodied and coupled with a given internal or external
environment, they become specific and adapted. Neurorobotics offers a
novel methodology to understand this process by considering both neural
systems and embodiment as experimental variables.
Since the 1950s, robots were essentially seen as fixed bodies on which
different programs could be plugged in, like the software and hardware
parts of a computer. This dualism has led to a prejudicial divergence
between artificial intelligence researchers building intelligent
programs and roboticists building sophisticated bodies. In the last
1980s, a handful of researchers tried to escape from what appeared to
be a technological dead-end and pushed forward a reunited view of
intelligence, under the name of embodied artificial intelligence or new
A.I. They argued that physical bodies and control systems should be
intrinsically linked, like two sides of the same coin. The return to
the design of complete agents undoubtedly led to some successes,
notably for locomotion, sensorimotor learning and navigation in unknown
complex environments. However, if such kind of holistic approaches
proved to be efficient for designing complex adapted behavior, they
were not sufficient to articulate a clear view of developmental
processes. For instance, in just a few months, children learn to crawl,
stand, walk, jump, hop, run, etc. As they learn these new skills in a
continuous incremental manner, their sensorimotor space changes
permitting them to investigate novel domains of exploration. This is
even clearer with the use of tools or the acquisition of communication
skills. This has lead researchers in developmental and epigenetic
robotics to present models in which an agent is essentially constituted
on the one hand, of a kernel, a set of stable processes that drive
developmental dynamics and, on the other hand, of variable body
envelopes that change over time. This novel view reverses the classic
notion of a fixed body on which different software can be applied to
consider a fixed software that can be applied to different kinds of
embodiment, potentially changing over time.
Experiments in neurorobotics permit to progress in understanding how
the interplay between neural learning dynamics, physical embodiment and
environment factors shape developmental trajectories in specific ways,
leading, in some circumstances, to open-ended acquisition of new
competences but also, in other cases, to abnormal trajectories. In
addition, neurorobotics is expected to provide the technological basis
for self-developing devices of a new kind, capable of acquiring new
know-how in a continuous and open-ended manner. Understanding how to
design such devices and the impact they can have in our daily
activities is ofa major importance for the coming years. |
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