Major walking robot breakthrough - based on 70 year old observations
by a Russian physiologist.


Robot that uses nested control loops with local fixed programs and
overall learning program to produce human like walking. The basic
local plus high level system of walking found in nature was first
described in the 1930's by Nikolai Bernstein.

Promises major breakthroughs in machine walking systems.
There's enough information in this short page alone to allow amateur
researchers to greatly advance the current state of the art in two
legged walking systems. So, the fact that the whole paper is also
available under a creative commons agreement is a very major bonus!
Well done on all counts!!!
Some (few) here will consider this is a terrible thing to do :-).




BBC

        http://news.bbc.co.uk/2/hi/technology/6291746.stm

Full paper.
Many figures, tables and references.
Superb robot resource.

        Adaptive Fast Walking in a Biped Robot under Neuronal Control
and Learning
            <http://compbiol.plosjournals.org/perlserv/?request=3Dget-docum=
ent&doi=3D10.1371/journal.pcbi.0030134>


            Russell

BBC ref - Matthew McMahon

Gratifyingly:

 Copyright: =A9 2007 Manoonpong et al. This is an open-access article =

distributed under the terms of the Creative Commons Attribution =

License, which permits unrestricted use, distribution, and =

reproduction in any medium, provided the original author and source =

are credited.

Citation:   Manoonpong P, Geng T, Kulvicius T, Porr B, W=F6rg=F6tter F =

(2007) Adaptive, Fast Walking in a Biped Robot under Neuronal Control =

and Learning. PLoS Comput Biol 3(7): e134 =

doi:10.1371/journal.pcbi.0030134









_____________________

Human walking is a dynamic, partly self-stabilizing process relying on
the interaction of the biomechanical design with its neuronal control.
The coordination of this process is a very difficult problem, and it
has been suggested that it involves a hierarchy of levels, where the
lower ones, e.g., interactions between muscles and the spinal cord,
are largely autonomous, and where higher level control (e.g.,
cortical) arises only pointwise, as needed. This requires an
architecture of several nested, sensori-motor loops where the walking
process provides feedback signals to the walker's sensory systems,
which can be used to coordinate its movements. To complicate the
situation, at a maximal walking speed of more than four leg-lengths
per second, the cycle period available to coordinate all these loops
is rather short. In this study we present a planar biped robot, which
uses the design principle of nested loops to combine the
self-stabilizing properties of its biomechanical design with several
levels of neuronal control. Specifically, we show how to adapt control
by including online learning mechanisms based on simulated synaptic
plasticity. This robot can walk with a high speed (>3.0 leg length/s),
self-adapting to minor disturbances, and reacting in a robust way to
abruptly induced gait changes. At the same time, it can learn walking
on different terrains, requiring only few learning experiences. This
study shows that the tight coupling of physical with neuronal control,
guided by sensory feedback from the walking pattern itself, combined
with synaptic learning may be a way forward to better understand and
solve coordination problems in other complex motor tasks.



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