582 research outputs found
Multiple chaotic central pattern generators with learning for legged locomotion and malfunction compensation
An originally chaotic system can be controlled into various periodic
dynamics. When it is implemented into a legged robot's locomotion control as a
central pattern generator (CPG), sophisticated gait patterns arise so that the
robot can perform various walking behaviors. However, such a single chaotic CPG
controller has difficulties dealing with leg malfunction. Specifically, in the
scenarios presented here, its movement permanently deviates from the desired
trajectory. To address this problem, we extend the single chaotic CPG to
multiple CPGs with learning. The learning mechanism is based on a simulated
annealing algorithm. In a normal situation, the CPGs synchronize and their
dynamics are identical. With leg malfunction or disability, the CPGs lose
synchronization leading to independent dynamics. In this case, the learning
mechanism is applied to automatically adjust the remaining legs' oscillation
frequencies so that the robot adapts its locomotion to deal with the
malfunction. As a consequence, the trajectory produced by the multiple chaotic
CPGs resembles the original trajectory far better than the one produced by only
a single CPG. The performance of the system is evaluated first in a physical
simulation of a quadruped as well as a hexapod robot and finally in a real
six-legged walking machine called AMOSII. The experimental results presented
here reveal that using multiple CPGs with learning is an effective approach for
adaptive locomotion generation where, for instance, different body parts have
to perform independent movements for malfunction compensation.Comment: 48 pages, 16 figures, Information Sciences 201
A SpiNNaker Application: Design, Implementation and Validation of SCPGs
In this paper, we present the numerical results of the implementation
of a Spiking Central Pattern Generator (SCPG) on a SpiNNaker
board. The SCPG is a network of current-based leaky integrateand-
fire (LIF) neurons, which generates periodic spike trains that correspond
to different locomotion gaits (i.e. walk, trot, run). To generate
such patterns, the SCPG has been configured with different topologies,
and its parameters have been experimentally estimated. To validate our
designs, we have implemented them on the SpiNNaker board using PyNN
and we have embedded it on a hexapod robot. The system includes a
Dynamic Vision Sensor system able to command a pattern to the robot
depending on the frequency of the events fired. The more activity the
DVS produces, the faster that the pattern that is commanded will be.Ministerio de Economía y Competitividad TEC2016-77785-
NeuroPod: a real-time neuromorphic spiking CPG applied to robotics
Initially, robots were developed with the aim of making our life easier, carrying
out repetitive or dangerous tasks for humans. Although they were able
to perform these tasks, the latest generation of robots are being designed
to take a step further, by performing more complex tasks that have been
carried out by smart animals or humans up to date. To this end, inspiration
needs to be taken from biological examples. For instance, insects are able
to optimally solve complex environment navigation problems, and many researchers
have started to mimic how these insects behave. Recent interest in
neuromorphic engineering has motivated us to present a real-time, neuromorphic,
spike-based Central Pattern Generator of application in neurorobotics,
using an arthropod-like robot. A Spiking Neural Network was designed and
implemented on SpiNNaker. The network models a complex, online-change
capable Central Pattern Generator which generates three gaits for a hexapod
robot locomotion. Recon gurable hardware was used to manage both
the motors of the robot and the real-time communication interface with the
Spiking Neural Networks. Real-time measurements con rm the simulation
results, and locomotion tests show that NeuroPod can perform the gaits
without any balance loss or added delay.Ministerio de Economía y Competitividad TEC2016-77785-
Evolvability signatures of generative encodings: beyond standard performance benchmarks
Evolutionary robotics is a promising approach to autonomously synthesize
machines with abilities that resemble those of animals, but the field suffers
from a lack of strong foundations. In particular, evolutionary systems are
currently assessed solely by the fitness score their evolved artifacts can
achieve for a specific task, whereas such fitness-based comparisons provide
limited insights about how the same system would evaluate on different tasks,
and its adaptive capabilities to respond to changes in fitness (e.g., from
damages to the machine, or in new situations). To counter these limitations, we
introduce the concept of "evolvability signatures", which picture the
post-mutation statistical distribution of both behavior diversity (how
different are the robot behaviors after a mutation?) and fitness values (how
different is the fitness after a mutation?). We tested the relevance of this
concept by evolving controllers for hexapod robot locomotion using five
different genotype-to-phenotype mappings (direct encoding, generative encoding
of open-loop and closed-loop central pattern generators, generative encoding of
neural networks, and single-unit pattern generators (SUPG)). We observed a
predictive relationship between the evolvability signature of each encoding and
the number of generations required by hexapods to adapt from incurred damages.
Our study also reveals that, across the five investigated encodings, the SUPG
scheme achieved the best evolvability signature, and was always foremost in
recovering an effective gait following robot damages. Overall, our evolvability
signatures neatly complement existing task-performance benchmarks, and pave the
way for stronger foundations for research in evolutionary robotics.Comment: 24 pages with 12 figures in the main text, and 4 supplementary
figures. Accepted at Information Sciences journal (in press). Supplemental
videos are available online at, see http://goo.gl/uyY1R
A novel plasticity rule can explain the development of sensorimotor intelligence
Grounding autonomous behavior in the nervous system is a fundamental
challenge for neuroscience. In particular, the self-organized behavioral
development provides more questions than answers. Are there special functional
units for curiosity, motivation, and creativity? This paper argues that these
features can be grounded in synaptic plasticity itself, without requiring any
higher level constructs. We propose differential extrinsic plasticity (DEP) as
a new synaptic rule for self-learning systems and apply it to a number of
complex robotic systems as a test case. Without specifying any purpose or goal,
seemingly purposeful and adaptive behavior is developed, displaying a certain
level of sensorimotor intelligence. These surprising results require no system
specific modifications of the DEP rule but arise rather from the underlying
mechanism of spontaneous symmetry breaking due to the tight
brain-body-environment coupling. The new synaptic rule is biologically
plausible and it would be an interesting target for a neurobiolocal
investigation. We also argue that this neuronal mechanism may have been a
catalyst in natural evolution.Comment: 18 pages, 5 figures, 7 video
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