---
_id: '6012'
abstract:
- lang: eng
text: We present an approach to identify concise equations from data using a shallow
neural network approach. In contrast to ordinary black-box regression, this approach
allows understanding functional relations and generalizing them from observed
data to unseen parts of the parameter space. We show how to extend the class of
learnable equations for a recently proposed equation learning network to include
divisions, and we improve the learning and model selection strategy to be useful
for challenging real-world data. For systems governed by analytical expressions,
our method can in many cases identify the true underlying equation and extrapolate
to unseen domains. We demonstrate its effectiveness by experiments on a cart-pendulum
system, where only 2 random rollouts are required to learn the forward dynamics
and successfully achieve the swing-up task.
article_processing_charge: No
author:
- first_name: Subham
full_name: Sahoo, Subham
last_name: Sahoo
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
- first_name: Georg S
full_name: Martius, Georg S
id: 3A276B68-F248-11E8-B48F-1D18A9856A87
last_name: Martius
citation:
ama: 'Sahoo S, Lampert C, Martius GS. Learning equations for extrapolation and control.
In: Proceedings of the 35th International Conference on Machine Learning.
Vol 80. ML Research Press; 2018:4442-4450.'
apa: 'Sahoo, S., Lampert, C., & Martius, G. S. (2018). Learning equations for
extrapolation and control. In Proceedings of the 35th International Conference
on Machine Learning (Vol. 80, pp. 4442–4450). Stockholm, Sweden: ML Research
Press.'
chicago: Sahoo, Subham, Christoph Lampert, and Georg S Martius. “Learning Equations
for Extrapolation and Control.” In Proceedings of the 35th International Conference
on Machine Learning, 80:4442–50. ML Research Press, 2018.
ieee: S. Sahoo, C. Lampert, and G. S. Martius, “Learning equations for extrapolation
and control,” in Proceedings of the 35th International Conference on Machine
Learning, Stockholm, Sweden, 2018, vol. 80, pp. 4442–4450.
ista: 'Sahoo S, Lampert C, Martius GS. 2018. Learning equations for extrapolation
and control. Proceedings of the 35th International Conference on Machine Learning.
ICML: International Conference on Machine Learning vol. 80, 4442–4450.'
mla: Sahoo, Subham, et al. “Learning Equations for Extrapolation and Control.” Proceedings
of the 35th International Conference on Machine Learning, vol. 80, ML Research
Press, 2018, pp. 4442–50.
short: S. Sahoo, C. Lampert, G.S. Martius, in:, Proceedings of the 35th International
Conference on Machine Learning, ML Research Press, 2018, pp. 4442–4450.
conference:
end_date: 2018-07-15
location: Stockholm, Sweden
name: 'ICML: International Conference on Machine Learning'
start_date: 2018-07-10
date_created: 2019-02-14T15:21:07Z
date_published: 2018-02-01T00:00:00Z
date_updated: 2023-10-17T09:50:53Z
day: '01'
department:
- _id: ChLa
ec_funded: 1
external_id:
arxiv:
- '1806.07259'
isi:
- '000683379204058'
intvolume: ' 80'
isi: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/1806.07259
month: '02'
oa: 1
oa_version: Preprint
page: 4442-4450
project:
- _id: 25681D80-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '291734'
name: International IST Postdoc Fellowship Programme
publication: Proceedings of the 35th International Conference on Machine Learning
publication_status: published
publisher: ML Research Press
quality_controlled: '1'
related_material:
link:
- description: News on IST Homepage
relation: press_release
url: https://ist.ac.at/en/news/first-machine-learning-method-capable-of-accurate-extrapolation/
scopus_import: '1'
status: public
title: Learning equations for extrapolation and control
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 80
year: '2018'
...
---
_id: '5584'
abstract:
- lang: eng
text: "This package contains data for the publication \"Nonlinear decoding of a
complex movie from the mammalian retina\" by Deny S. et al, PLOS Comput Biol (2018).
\r\n\r\nThe data consists of\r\n(i) 91 spike sorted, isolated rat retinal ganglion
cells that pass stability and quality criteria, recorded on the multi-electrode
array, in response to the presentation of the complex movie with many randomly
moving dark discs. The responses are represented as 648000 x 91 binary matrix,
where the first index indicates the timebin of duration 12.5 ms, and the second
index the neural identity. The matrix entry is 0/1 if the neuron didn't/did spike
in the particular time bin.\r\n(ii) README file and a graphical illustration of
the structure of the experiment, specifying how the 648000 timebins are split
into epochs where 1, 2, 4, or 10 discs were displayed, and which stimulus segments
are exact repeats or unique ball trajectories.\r\n(iii) a 648000 x 400 matrix
of luminance traces for each of the 20 x 20 positions (\"sites\") in the movie
frame, with time that is locked to the recorded raster. The luminance traces are
produced as described in the manuscript by filtering the raw disc movie with a
small gaussian spatial kernel. "
article_processing_charge: No
author:
- first_name: Stephane
full_name: Deny, Stephane
last_name: Deny
- first_name: Olivier
full_name: Marre, Olivier
last_name: Marre
- first_name: Vicente
full_name: Botella-Soler, Vicente
last_name: Botella-Soler
- first_name: Georg S
full_name: Martius, Georg S
id: 3A276B68-F248-11E8-B48F-1D18A9856A87
last_name: Martius
- first_name: Gasper
full_name: Tkacik, Gasper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkacik
orcid: 0000-0002-6699-1455
citation:
ama: Deny S, Marre O, Botella-Soler V, Martius GS, Tkačik G. Nonlinear decoding
of a complex movie from the mammalian retina. 2018. doi:10.15479/AT:ISTA:98
apa: Deny, S., Marre, O., Botella-Soler, V., Martius, G. S., & Tkačik, G. (2018).
Nonlinear decoding of a complex movie from the mammalian retina. Institute of
Science and Technology Austria. https://doi.org/10.15479/AT:ISTA:98
chicago: Deny, Stephane, Olivier Marre, Vicente Botella-Soler, Georg S Martius,
and Gašper Tkačik. “Nonlinear Decoding of a Complex Movie from the Mammalian Retina.”
Institute of Science and Technology Austria, 2018. https://doi.org/10.15479/AT:ISTA:98.
ieee: S. Deny, O. Marre, V. Botella-Soler, G. S. Martius, and G. Tkačik, “Nonlinear
decoding of a complex movie from the mammalian retina.” Institute of Science and
Technology Austria, 2018.
ista: Deny S, Marre O, Botella-Soler V, Martius GS, Tkačik G. 2018. Nonlinear decoding
of a complex movie from the mammalian retina, Institute of Science and Technology
Austria, 10.15479/AT:ISTA:98.
mla: Deny, Stephane, et al. Nonlinear Decoding of a Complex Movie from the Mammalian
Retina. Institute of Science and Technology Austria, 2018, doi:10.15479/AT:ISTA:98.
short: S. Deny, O. Marre, V. Botella-Soler, G.S. Martius, G. Tkačik, (2018).
datarep_id: '98'
date_created: 2018-12-12T12:31:39Z
date_published: 2018-03-29T00:00:00Z
date_updated: 2024-02-21T13:45:26Z
day: '29'
ddc:
- '570'
department:
- _id: ChLa
- _id: GaTk
doi: 10.15479/AT:ISTA:98
file:
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checksum: 6808748837b9afbbbabc2a356ca2b88a
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creator: system
date_created: 2018-12-12T13:02:24Z
date_updated: 2020-07-14T12:47:07Z
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date_created: 2018-12-12T13:02:26Z
date_updated: 2020-07-14T12:47:07Z
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date_created: 2018-12-12T13:02:26Z
date_updated: 2020-07-14T12:47:07Z
file_id: '5593'
file_name: IST-2018-98-v1+4_README.txt
file_size: 986
relation: main_file
file_date_updated: 2020-07-14T12:47:07Z
has_accepted_license: '1'
keyword:
- retina
- decoding
- regression
- neural networks
- complex stimulus
license: https://creativecommons.org/publicdomain/zero/1.0/
month: '03'
oa: 1
oa_version: Published Version
project:
- _id: 254D1A94-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: P 25651-N26
name: Sensitivity to higher-order statistics in natural scenes
publisher: Institute of Science and Technology Austria
related_material:
record:
- id: '292'
relation: used_in_publication
status: public
status: public
title: Nonlinear decoding of a complex movie from the mammalian retina
tmp:
image: /images/cc_0.png
legal_code_url: https://creativecommons.org/publicdomain/zero/1.0/legalcode
name: Creative Commons Public Domain Dedication (CC0 1.0)
short: CC0 (1.0)
type: research_data
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2018'
...
---
_id: '652'
abstract:
- lang: eng
text: 'We present an approach that enables robots to self-organize their sensorimotor
behavior from scratch without providing specific information about neither the
robot nor its environment. This is achieved by a simple neural control law that
increases the consistency between external sensor dynamics and internal neural
dynamics of the utterly simple controller. In this way, the embodiment and the
agent-environment coupling are the only source of individual development. We show
how an anthropomorphic tendon driven arm-shoulder system develops different behaviors
depending on that coupling. For instance: Given a bottle half-filled with water,
the arm starts to shake it, driven by the physical response of the water. When
attaching a brush, the arm can be manipulated into wiping a table, and when connected
to a revolvable wheel it finds out how to rotate it. Thus, the robot may be said
to discover the affordances of the world. When allowing two (simulated) humanoid
robots to interact physically, they engage into a joint behavior development leading
to, for instance, spontaneous cooperation. More social effects are observed if
the robots can visually perceive each other. Although, as an observer, it is tempting
to attribute an apparent intentionality, there is nothing of the kind put in.
As a conclusion, we argue that emergent behavior may be much less rooted in explicit
intentions, internal motivations, or specific reward systems than is commonly
believed.'
article_number: '7846789'
author:
- first_name: Ralf
full_name: Der, Ralf
last_name: Der
- first_name: Georg S
full_name: Martius, Georg S
id: 3A276B68-F248-11E8-B48F-1D18A9856A87
last_name: Martius
citation:
ama: 'Der R, Martius GS. Dynamical self consistency leads to behavioral development
and emergent social interactions in robots. In: IEEE; 2017. doi:10.1109/DEVLRN.2016.7846789'
apa: 'Der, R., & Martius, G. S. (2017). Dynamical self consistency leads to
behavioral development and emergent social interactions in robots. Presented at
the ICDL EpiRob: International Conference on Development and Learning and Epigenetic
Robotics , Cergy-Pontoise, France: IEEE. https://doi.org/10.1109/DEVLRN.2016.7846789'
chicago: Der, Ralf, and Georg S Martius. “Dynamical Self Consistency Leads to Behavioral
Development and Emergent Social Interactions in Robots.” IEEE, 2017. https://doi.org/10.1109/DEVLRN.2016.7846789.
ieee: 'R. Der and G. S. Martius, “Dynamical self consistency leads to behavioral
development and emergent social interactions in robots,” presented at the ICDL
EpiRob: International Conference on Development and Learning and Epigenetic Robotics
, Cergy-Pontoise, France, 2017.'
ista: 'Der R, Martius GS. 2017. Dynamical self consistency leads to behavioral development
and emergent social interactions in robots. ICDL EpiRob: International Conference
on Development and Learning and Epigenetic Robotics , 7846789.'
mla: Der, Ralf, and Georg S. Martius. Dynamical Self Consistency Leads to Behavioral
Development and Emergent Social Interactions in Robots. 7846789, IEEE, 2017,
doi:10.1109/DEVLRN.2016.7846789.
short: R. Der, G.S. Martius, in:, IEEE, 2017.
conference:
end_date: 2016-09-22
location: Cergy-Pontoise, France
name: 'ICDL EpiRob: International Conference on Development and Learning and Epigenetic
Robotics '
start_date: 2016-09-19
date_created: 2018-12-11T11:47:43Z
date_published: 2017-02-07T00:00:00Z
date_updated: 2021-01-12T08:07:51Z
day: '07'
department:
- _id: ChLa
- _id: GaTk
doi: 10.1109/DEVLRN.2016.7846789
language:
- iso: eng
month: '02'
oa_version: None
publication_identifier:
isbn:
- 978-150905069-7
publication_status: published
publisher: IEEE
publist_id: '7100'
quality_controlled: '1'
scopus_import: 1
status: public
title: Dynamical self consistency leads to behavioral development and emergent social
interactions in robots
type: conference
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
year: '2017'
...
---
_id: '658'
abstract:
- lang: eng
text: 'With the accelerated development of robot technologies, control becomes one
of the central themes of research. In traditional approaches, the controller,
by its internal functionality, finds appropriate actions on the basis of specific
objectives for the task at hand. While very successful in many applications, self-organized
control schemes seem to be favored in large complex systems with unknown dynamics
or which are difficult to model. Reasons are the expected scalability, robustness,
and resilience of self-organizing systems. The paper presents a self-learning
neurocontroller based on extrinsic differential plasticity introduced recently,
applying it to an anthropomorphic musculoskeletal robot arm with attached objects
of unknown physical dynamics. The central finding of the paper is the following
effect: by the mere feedback through the internal dynamics of the object, the
robot is learning to relate each of the objects with a very specific sensorimotor
pattern. Specifically, an attached pendulum pilots the arm into a circular motion,
a half-filled bottle produces axis oriented shaking behavior, a wheel is getting
rotated, and wiping patterns emerge automatically in a table-plus-brush setting.
By these object-specific dynamical patterns, the robot may be said to recognize
the object''s identity, or in other words, it discovers dynamical affordances
of objects. Furthermore, when including hand coordinates obtained from a camera,
a dedicated hand-eye coordination self-organizes spontaneously. These phenomena
are discussed from a specific dynamical system perspective. Central is the dedicated
working regime at the border to instability with its potentially infinite reservoir
of (limit cycle) attractors "waiting" to be excited. Besides converging
toward one of these attractors, variate behavior is also arising from a self-induced
attractor morphing driven by the learning rule. We claim that experimental investigations
with this anthropomorphic, self-learning robot not only generate interesting and
potentially useful behaviors, but may also help to better understand what subjective
human muscle feelings are, how they can be rooted in sensorimotor patterns, and
how these concepts may feed back on robotics.'
article_number: '00008'
article_processing_charge: Yes
author:
- first_name: Ralf
full_name: Der, Ralf
last_name: Der
- first_name: Georg S
full_name: Martius, Georg S
id: 3A276B68-F248-11E8-B48F-1D18A9856A87
last_name: Martius
citation:
ama: Der R, Martius GS. Self organized behavior generation for musculoskeletal robots.
Frontiers in Neurorobotics. 2017;11(MAR). doi:10.3389/fnbot.2017.00008
apa: Der, R., & Martius, G. S. (2017). Self organized behavior generation for
musculoskeletal robots. Frontiers in Neurorobotics. Frontiers Research
Foundation. https://doi.org/10.3389/fnbot.2017.00008
chicago: Der, Ralf, and Georg S Martius. “Self Organized Behavior Generation for
Musculoskeletal Robots.” Frontiers in Neurorobotics. Frontiers Research
Foundation, 2017. https://doi.org/10.3389/fnbot.2017.00008.
ieee: R. Der and G. S. Martius, “Self organized behavior generation for musculoskeletal
robots,” Frontiers in Neurorobotics, vol. 11, no. MAR. Frontiers Research
Foundation, 2017.
ista: Der R, Martius GS. 2017. Self organized behavior generation for musculoskeletal
robots. Frontiers in Neurorobotics. 11(MAR), 00008.
mla: Der, Ralf, and Georg S. Martius. “Self Organized Behavior Generation for Musculoskeletal
Robots.” Frontiers in Neurorobotics, vol. 11, no. MAR, 00008, Frontiers
Research Foundation, 2017, doi:10.3389/fnbot.2017.00008.
short: R. Der, G.S. Martius, Frontiers in Neurorobotics 11 (2017).
date_created: 2018-12-11T11:47:45Z
date_published: 2017-03-16T00:00:00Z
date_updated: 2021-01-12T08:08:04Z
day: '16'
ddc:
- '006'
department:
- _id: ChLa
- _id: GaTk
doi: 10.3389/fnbot.2017.00008
ec_funded: 1
file:
- access_level: open_access
checksum: b1bc43f96d1df3313c03032c2a46388d
content_type: application/pdf
creator: system
date_created: 2018-12-12T10:18:49Z
date_updated: 2020-07-14T12:47:33Z
file_id: '5371'
file_name: IST-2017-903-v1+1_fnbot-11-00008.pdf
file_size: 8439566
relation: main_file
file_date_updated: 2020-07-14T12:47:33Z
has_accepted_license: '1'
intvolume: ' 11'
issue: MAR
language:
- iso: eng
license: https://creativecommons.org/licenses/by/4.0/
month: '03'
oa: 1
oa_version: Published Version
project:
- _id: 25681D80-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '291734'
name: International IST Postdoc Fellowship Programme
publication: Frontiers in Neurorobotics
publication_identifier:
issn:
- '16625218'
publication_status: published
publisher: Frontiers Research Foundation
publist_id: '7078'
pubrep_id: '903'
quality_controlled: '1'
scopus_import: 1
status: public
title: Self organized behavior generation for musculoskeletal robots
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 2EBD1598-F248-11E8-B48F-1D18A9856A87
volume: 11
year: '2017'
...
---
_id: '6841'
abstract:
- lang: eng
text: In classical machine learning, regression is treated as a black box process
of identifying a suitable function from a hypothesis set without attempting to
gain insight into the mechanism connecting inputs and outputs. In the natural
sciences, however, finding an interpretable function for a phenomenon is the prime
goal as it allows to understand and generalize results. This paper proposes a
novel type of function learning network, called equation learner (EQL), that can
learn analytical expressions and is able to extrapolate to unseen domains. It
is implemented as an end-to-end differentiable feed-forward network and allows
for efficient gradient based training. Due to sparsity regularization concise
interpretable expressions can be obtained. Often the true underlying source expression
is identified.
author:
- first_name: Georg S
full_name: Martius, Georg S
id: 3A276B68-F248-11E8-B48F-1D18A9856A87
last_name: Martius
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Martius GS, Lampert C. Extrapolation and learning equations. In: 5th International
Conference on Learning Representations, ICLR 2017 - Workshop Track Proceedings.
International Conference on Learning Representations; 2017.'
apa: 'Martius, G. S., & Lampert, C. (2017). Extrapolation and learning equations.
In 5th International Conference on Learning Representations, ICLR 2017 - Workshop
Track Proceedings. Toulon, France: International Conference on Learning Representations.'
chicago: Martius, Georg S, and Christoph Lampert. “Extrapolation and Learning Equations.”
In 5th International Conference on Learning Representations, ICLR 2017 - Workshop
Track Proceedings. International Conference on Learning Representations, 2017.
ieee: G. S. Martius and C. Lampert, “Extrapolation and learning equations,” in 5th
International Conference on Learning Representations, ICLR 2017 - Workshop Track
Proceedings, Toulon, France, 2017.
ista: 'Martius GS, Lampert C. 2017. Extrapolation and learning equations. 5th International
Conference on Learning Representations, ICLR 2017 - Workshop Track Proceedings.
ICLR: International Conference on Learning Representations.'
mla: Martius, Georg S., and Christoph Lampert. “Extrapolation and Learning Equations.”
5th International Conference on Learning Representations, ICLR 2017 - Workshop
Track Proceedings, International Conference on Learning Representations, 2017.
short: G.S. Martius, C. Lampert, in:, 5th International Conference on Learning Representations,
ICLR 2017 - Workshop Track Proceedings, International Conference on Learning Representations,
2017.
conference:
end_date: 2017-04-26
location: Toulon, France
name: 'ICLR: International Conference on Learning Representations'
start_date: 2017-04-24
date_created: 2019-09-01T22:01:00Z
date_published: 2017-02-21T00:00:00Z
date_updated: 2021-01-12T08:09:17Z
day: '21'
department:
- _id: ChLa
ec_funded: 1
external_id:
arxiv:
- '1610.02995'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/1610.02995
month: '02'
oa: 1
oa_version: Preprint
project:
- _id: 2532554C-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '308036'
name: Lifelong Learning of Visual Scene Understanding
publication: 5th International Conference on Learning Representations, ICLR 2017 -
Workshop Track Proceedings
publication_status: published
publisher: International Conference on Learning Representations
quality_controlled: '1'
scopus_import: 1
status: public
title: Extrapolation and learning equations
type: conference
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
year: '2017'
...
---
_id: '1214'
abstract:
- lang: eng
text: 'With the accelerated development of robot technologies, optimal control becomes
one of the central themes of research. In traditional approaches, the controller,
by its internal functionality, finds appropriate actions on the basis of the history
of sensor values, guided by the goals, intentions, objectives, learning schemes,
and so forth. While very successful with classical robots, these methods run into
severe difficulties when applied to soft robots, a new field of robotics with
large interest for human-robot interaction. We claim that a novel controller paradigm
opens new perspective for this field. This paper applies a recently developed
neuro controller with differential extrinsic synaptic plasticity to a muscle-tendon
driven arm-shoulder system from the Myorobotics toolkit. In the experiments, we
observe a vast variety of self-organized behavior patterns: when left alone, the
arm realizes pseudo-random sequences of different poses. By applying physical
forces, the system can be entrained into definite motion patterns like wiping
a table. Most interestingly, after attaching an object, the controller gets in
a functional resonance with the object''s internal dynamics, starting to shake
spontaneously bottles half-filled with water or sensitively driving an attached
pendulum into a circular mode. When attached to the crank of a wheel the neural
system independently develops to rotate it. In this way, the robot discovers affordances
of objects its body is interacting with.'
acknowledgement: RD thanks for the hospitality at the Max-Planck-Institute and for
helpful discussions with Nihat Ay and Keyan Zahedi.
article_number: '7759138'
author:
- first_name: Georg S
full_name: Martius, Georg S
id: 3A276B68-F248-11E8-B48F-1D18A9856A87
last_name: Martius
- first_name: Raphael
full_name: Hostettler, Raphael
last_name: Hostettler
- first_name: Alois
full_name: Knoll, Alois
last_name: Knoll
- first_name: Ralf
full_name: Der, Ralf
last_name: Der
citation:
ama: 'Martius GS, Hostettler R, Knoll A, Der R. Compliant control for soft robots:
Emergent behavior of a tendon driven anthropomorphic arm. In: Vol 2016-November.
IEEE; 2016. doi:10.1109/IROS.2016.7759138'
apa: 'Martius, G. S., Hostettler, R., Knoll, A., & Der, R. (2016). Compliant
control for soft robots: Emergent behavior of a tendon driven anthropomorphic
arm (Vol. 2016–November). Presented at the IEEE RSJ International Conference on
Intelligent Robots and Systems IROS , Daejeon, Korea: IEEE. https://doi.org/10.1109/IROS.2016.7759138'
chicago: 'Martius, Georg S, Raphael Hostettler, Alois Knoll, and Ralf Der. “Compliant
Control for Soft Robots: Emergent Behavior of a Tendon Driven Anthropomorphic
Arm,” Vol. 2016–November. IEEE, 2016. https://doi.org/10.1109/IROS.2016.7759138.'
ieee: 'G. S. Martius, R. Hostettler, A. Knoll, and R. Der, “Compliant control for
soft robots: Emergent behavior of a tendon driven anthropomorphic arm,” presented
at the IEEE RSJ International Conference on Intelligent Robots and Systems IROS
, Daejeon, Korea, 2016, vol. 2016–November.'
ista: 'Martius GS, Hostettler R, Knoll A, Der R. 2016. Compliant control for soft
robots: Emergent behavior of a tendon driven anthropomorphic arm. IEEE RSJ International
Conference on Intelligent Robots and Systems IROS vol. 2016–November, 7759138.'
mla: 'Martius, Georg S., et al. Compliant Control for Soft Robots: Emergent Behavior
of a Tendon Driven Anthropomorphic Arm. Vol. 2016–November, 7759138, IEEE,
2016, doi:10.1109/IROS.2016.7759138.'
short: G.S. Martius, R. Hostettler, A. Knoll, R. Der, in:, IEEE, 2016.
conference:
end_date: 2016-09-14
location: Daejeon, Korea
name: 'IEEE RSJ International Conference on Intelligent Robots and Systems IROS '
start_date: 2016-09-09
date_created: 2018-12-11T11:50:45Z
date_published: 2016-11-28T00:00:00Z
date_updated: 2021-01-12T06:49:08Z
day: '28'
department:
- _id: ChLa
- _id: GaTk
doi: 10.1109/IROS.2016.7759138
language:
- iso: eng
month: '11'
oa_version: None
publication_status: published
publisher: IEEE
publist_id: '6121'
quality_controlled: '1'
scopus_import: 1
status: public
title: 'Compliant control for soft robots: Emergent behavior of a tendon driven anthropomorphic
arm'
type: conference
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
volume: 2016-November
year: '2016'
...
---
_id: '8094'
abstract:
- lang: eng
text: 'With the accelerated development of robot technologies, optimal control becomes
one of the central themes of research. In traditional approaches, the controller,
by its internal functionality, finds appropriate actions on the basis of the history
of sensor values, guided by the goals, intentions, objectives, learning schemes,
and so forth. The idea is that the controller controls the world---the body plus
its environment---as reliably as possible. This paper focuses on new lines of
self-organization for developmental robotics. We apply the recently developed
differential extrinsic synaptic plasticity to a muscle-tendon driven arm-shoulder
system from the Myorobotics toolkit. In the experiments, we observe a vast variety
of self-organized behavior patterns: when left alone, the arm realizes pseudo-random
sequences of different poses. By applying physical forces, the system can be entrained
into definite motion patterns like wiping a table. Most interestingly, after attaching
an object, the controller gets in a functional resonance with the object''s internal
dynamics, starting to shake spontaneously bottles half-filled with water or sensitively
driving an attached pendulum into a circular mode. When attached to the crank
of a wheel the neural system independently discovers how to rotate it. In this
way, the robot discovers affordances of objects its body is interacting with.'
article_processing_charge: No
author:
- first_name: Georg S
full_name: Martius, Georg S
id: 3A276B68-F248-11E8-B48F-1D18A9856A87
last_name: Martius
- first_name: Rafael
full_name: Hostettler, Rafael
last_name: Hostettler
- first_name: Alois
full_name: Knoll, Alois
last_name: Knoll
- first_name: Ralf
full_name: Der, Ralf
last_name: Der
citation:
ama: 'Martius GS, Hostettler R, Knoll A, Der R. Self-organized control of an tendon
driven arm by differential extrinsic plasticity. In: Proceedings of the Artificial
Life Conference 2016. Vol 28. MIT Press; 2016:142-143. doi:10.7551/978-0-262-33936-0-ch029'
apa: 'Martius, G. S., Hostettler, R., Knoll, A., & Der, R. (2016). Self-organized
control of an tendon driven arm by differential extrinsic plasticity. In Proceedings
of the Artificial Life Conference 2016 (Vol. 28, pp. 142–143). Cancun, Mexico:
MIT Press. https://doi.org/10.7551/978-0-262-33936-0-ch029'
chicago: Martius, Georg S, Rafael Hostettler, Alois Knoll, and Ralf Der. “Self-Organized
Control of an Tendon Driven Arm by Differential Extrinsic Plasticity.” In Proceedings
of the Artificial Life Conference 2016, 28:142–43. MIT Press, 2016. https://doi.org/10.7551/978-0-262-33936-0-ch029.
ieee: G. S. Martius, R. Hostettler, A. Knoll, and R. Der, “Self-organized control
of an tendon driven arm by differential extrinsic plasticity,” in Proceedings
of the Artificial Life Conference 2016, Cancun, Mexico, 2016, vol. 28, pp.
142–143.
ista: 'Martius GS, Hostettler R, Knoll A, Der R. 2016. Self-organized control of
an tendon driven arm by differential extrinsic plasticity. Proceedings of the
Artificial Life Conference 2016. ALIFE 2016: 15th International Conference on
the Synthesis and Simulation of Living Systems vol. 28, 142–143.'
mla: Martius, Georg S., et al. “Self-Organized Control of an Tendon Driven Arm by
Differential Extrinsic Plasticity.” Proceedings of the Artificial Life Conference
2016, vol. 28, MIT Press, 2016, pp. 142–43, doi:10.7551/978-0-262-33936-0-ch029.
short: G.S. Martius, R. Hostettler, A. Knoll, R. Der, in:, Proceedings of the Artificial
Life Conference 2016, MIT Press, 2016, pp. 142–143.
conference:
end_date: 2016-07-08
location: Cancun, Mexico
name: 'ALIFE 2016: 15th International Conference on the Synthesis and Simulation
of Living Systems'
start_date: 2016-07-04
date_created: 2020-07-05T22:00:47Z
date_published: 2016-09-01T00:00:00Z
date_updated: 2021-01-12T08:16:53Z
day: '01'
ddc:
- '610'
department:
- _id: ChLa
- _id: GaTk
doi: 10.7551/978-0-262-33936-0-ch029
ec_funded: 1
file:
- access_level: open_access
checksum: cff63e7a4b8ac466ba51a9c84153a940
content_type: application/pdf
creator: cziletti
date_created: 2020-07-06T12:59:09Z
date_updated: 2020-07-14T12:48:09Z
file_id: '8096'
file_name: 2016_ProcALIFE_Martius.pdf
file_size: 678670
relation: main_file
file_date_updated: 2020-07-14T12:48:09Z
has_accepted_license: '1'
intvolume: ' 28'
language:
- iso: eng
month: '09'
oa: 1
oa_version: Published Version
page: 142-143
project:
- _id: 25681D80-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '291734'
name: International IST Postdoc Fellowship Programme
publication: Proceedings of the Artificial Life Conference 2016
publication_identifier:
isbn:
- '9780262339360'
publication_status: published
publisher: MIT Press
quality_controlled: '1'
scopus_import: 1
status: public
title: Self-organized control of an tendon driven arm by differential extrinsic plasticity
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: conference
user_id: D865714E-FA4E-11E9-B85B-F5C5E5697425
volume: 28
year: '2016'
...
---
_id: '1570'
abstract:
- lang: eng
text: Grounding autonomous behavior in the nervous system is a fundamental challenge
for neuroscience. In particular, 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
rhythmic behavior is developed, displaying a certain level of sensorimotor intelligence.
These surprising results require no systemspecific modifications of the DEP rule.
They rather arise from the underlying mechanism of spontaneous symmetry breaking,which
is due to the tight brain body environment coupling. The new synaptic rule is
biologically plausible and would be an interesting target for neurobiological
investigation. We also argue that this neuronal mechanism may have been a catalyst
in natural evolution.
author:
- first_name: Ralf
full_name: Der, Ralf
last_name: Der
- first_name: Georg S
full_name: Martius, Georg S
id: 3A276B68-F248-11E8-B48F-1D18A9856A87
last_name: Martius
citation:
ama: Der R, Martius GS. Novel plasticity rule can explain the development of sensorimotor
intelligence. PNAS. 2015;112(45):E6224-E6232. doi:10.1073/pnas.1508400112
apa: Der, R., & Martius, G. S. (2015). Novel plasticity rule can explain the
development of sensorimotor intelligence. PNAS. National Academy of Sciences.
https://doi.org/10.1073/pnas.1508400112
chicago: Der, Ralf, and Georg S Martius. “Novel Plasticity Rule Can Explain the
Development of Sensorimotor Intelligence.” PNAS. National Academy of Sciences,
2015. https://doi.org/10.1073/pnas.1508400112.
ieee: R. Der and G. S. Martius, “Novel plasticity rule can explain the development
of sensorimotor intelligence,” PNAS, vol. 112, no. 45. National Academy
of Sciences, pp. E6224–E6232, 2015.
ista: Der R, Martius GS. 2015. Novel plasticity rule can explain the development
of sensorimotor intelligence. PNAS. 112(45), E6224–E6232.
mla: Der, Ralf, and Georg S. Martius. “Novel Plasticity Rule Can Explain the Development
of Sensorimotor Intelligence.” PNAS, vol. 112, no. 45, National Academy
of Sciences, 2015, pp. E6224–32, doi:10.1073/pnas.1508400112.
short: R. Der, G.S. Martius, PNAS 112 (2015) E6224–E6232.
date_created: 2018-12-11T11:52:47Z
date_published: 2015-11-10T00:00:00Z
date_updated: 2021-01-12T06:51:40Z
day: '10'
department:
- _id: ChLa
- _id: GaTk
doi: 10.1073/pnas.1508400112
ec_funded: 1
external_id:
pmid:
- '26504200'
intvolume: ' 112'
issue: '45'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4653169/
month: '11'
oa: 1
oa_version: Submitted Version
page: E6224 - E6232
pmid: 1
project:
- _id: 25681D80-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '291734'
name: International IST Postdoc Fellowship Programme
publication: PNAS
publication_status: published
publisher: National Academy of Sciences
publist_id: '5601'
quality_controlled: '1'
scopus_import: 1
status: public
title: Novel plasticity rule can explain the development of sensorimotor intelligence
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 112
year: '2015'
...
---
_id: '12881'
acknowledgement: This work was supported by the DFG (SPP 1527) and the EU (FP7, REA
grant no 291734).
article_processing_charge: No
author:
- first_name: Georg S
full_name: Martius, Georg S
id: 3A276B68-F248-11E8-B48F-1D18A9856A87
last_name: Martius
- first_name: Eckehard
full_name: Olbrich, Eckehard
last_name: Olbrich
citation:
ama: 'Martius GS, Olbrich E. Quantifying self-organizing behavior of autonomous
robots. In: Proceedings of the 13th European Conference on Artificial Life.
MIT Press; 2015:78. doi:10.7551/978-0-262-33027-5-ch018'
apa: 'Martius, G. S., & Olbrich, E. (2015). Quantifying self-organizing behavior
of autonomous robots. In Proceedings of the 13th European Conference on Artificial
Life (p. 78). York, United Kingdom: MIT Press. https://doi.org/10.7551/978-0-262-33027-5-ch018'
chicago: Martius, Georg S, and Eckehard Olbrich. “Quantifying Self-Organizing Behavior
of Autonomous Robots.” In Proceedings of the 13th European Conference on Artificial
Life, 78. MIT Press, 2015. https://doi.org/10.7551/978-0-262-33027-5-ch018.
ieee: G. S. Martius and E. Olbrich, “Quantifying self-organizing behavior of autonomous
robots,” in Proceedings of the 13th European Conference on Artificial Life,
York, United Kingdom, 2015, p. 78.
ista: 'Martius GS, Olbrich E. 2015. Quantifying self-organizing behavior of autonomous
robots. Proceedings of the 13th European Conference on Artificial Life. ECAL:
European Conference on Artificial Life, 78.'
mla: Martius, Georg S., and Eckehard Olbrich. “Quantifying Self-Organizing Behavior
of Autonomous Robots.” Proceedings of the 13th European Conference on Artificial
Life, MIT Press, 2015, p. 78, doi:10.7551/978-0-262-33027-5-ch018.
short: G.S. Martius, E. Olbrich, in:, Proceedings of the 13th European Conference
on Artificial Life, MIT Press, 2015, p. 78.
conference:
end_date: 2015-07-24
location: York, United Kingdom
name: 'ECAL: European Conference on Artificial Life'
start_date: 2015-07-20
date_created: 2023-04-30T22:01:07Z
date_published: 2015-07-01T00:00:00Z
date_updated: 2023-05-02T07:06:21Z
day: '01'
ddc:
- '000'
department:
- _id: ChLa
doi: 10.7551/978-0-262-33027-5-ch018
ec_funded: 1
file:
- access_level: open_access
checksum: 880eabe59c9df12f06a882aa1bc4e600
content_type: application/pdf
creator: dernst
date_created: 2023-05-02T07:02:59Z
date_updated: 2023-05-02T07:02:59Z
file_id: '12882'
file_name: 2015_ECAL_Martius.pdf
file_size: 1674241
relation: main_file
success: 1
file_date_updated: 2023-05-02T07:02:59Z
has_accepted_license: '1'
language:
- iso: eng
month: '07'
oa: 1
oa_version: Published Version
page: '78'
project:
- _id: 25681D80-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '291734'
name: International IST Postdoc Fellowship Programme
publication: Proceedings of the 13th European Conference on Artificial Life
publication_identifier:
isbn:
- '9780262330275'
publication_status: published
publisher: MIT Press
quality_controlled: '1'
scopus_import: '1'
status: public
title: Quantifying self-organizing behavior of autonomous robots
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2015'
...
---
_id: '1655'
abstract:
- lang: eng
text: Quantifying behaviors of robots which were generated autonomously from task-independent
objective functions is an important prerequisite for objective comparisons of
algorithms and movements of animals. The temporal sequence of such a behavior
can be considered as a time series and hence complexity measures developed for
time series are natural candidates for its quantification. The predictive information
and the excess entropy are such complexity measures. They measure the amount of
information the past contains about the future and thus quantify the nonrandom
structure in the temporal sequence. However, when using these measures for systems
with continuous states one has to deal with the fact that their values will depend
on the resolution with which the systems states are observed. For deterministic
systems both measures will diverge with increasing resolution. We therefore propose
a new decomposition of the excess entropy in resolution dependent and resolution
independent parts and discuss how they depend on the dimensionality of the dynamics,
correlations and the noise level. For the practical estimation we propose to use
estimates based on the correlation integral instead of the direct estimation of
the mutual information based on next neighbor statistics because the latter allows
less control of the scale dependencies. Using our algorithm we are able to show
how autonomous learning generates behavior of increasing complexity with increasing
learning duration.
acknowledgement: This work was supported by the DFG priority program 1527 (Autonomous
Learning) and by the European Community’s Seventh Framework Programme (FP7/2007-2013)
under grant agreement no. 318723 (MatheMACS) and from the People Programme (Marie
Curie Actions) of the European Union’s Seventh Framework Programme (FP7/2007-2013)
under REA grant agreement no. 291734.
article_processing_charge: No
author:
- first_name: Georg S
full_name: Martius, Georg S
id: 3A276B68-F248-11E8-B48F-1D18A9856A87
last_name: Martius
- first_name: Eckehard
full_name: Olbrich, Eckehard
last_name: Olbrich
citation:
ama: Martius GS, Olbrich E. Quantifying emergent behavior of autonomous robots.
Entropy. 2015;17(10):7266-7297. doi:10.3390/e17107266
apa: Martius, G. S., & Olbrich, E. (2015). Quantifying emergent behavior of
autonomous robots. Entropy. MDPI. https://doi.org/10.3390/e17107266
chicago: Martius, Georg S, and Eckehard Olbrich. “Quantifying Emergent Behavior
of Autonomous Robots.” Entropy. MDPI, 2015. https://doi.org/10.3390/e17107266.
ieee: G. S. Martius and E. Olbrich, “Quantifying emergent behavior of autonomous
robots,” Entropy, vol. 17, no. 10. MDPI, pp. 7266–7297, 2015.
ista: Martius GS, Olbrich E. 2015. Quantifying emergent behavior of autonomous robots.
Entropy. 17(10), 7266–7297.
mla: Martius, Georg S., and Eckehard Olbrich. “Quantifying Emergent Behavior of
Autonomous Robots.” Entropy, vol. 17, no. 10, MDPI, 2015, pp. 7266–97,
doi:10.3390/e17107266.
short: G.S. Martius, E. Olbrich, Entropy 17 (2015) 7266–7297.
date_created: 2018-12-11T11:53:17Z
date_published: 2015-10-23T00:00:00Z
date_updated: 2023-10-17T11:42:00Z
day: '23'
ddc:
- '000'
department:
- _id: ChLa
- _id: GaTk
doi: 10.3390/e17107266
ec_funded: 1
file:
- access_level: open_access
checksum: 945d99631a96e0315acb26dc8541dcf9
content_type: application/pdf
creator: system
date_created: 2018-12-12T10:12:25Z
date_updated: 2020-07-14T12:45:08Z
file_id: '4943'
file_name: IST-2016-464-v1+1_entropy-17-07266.pdf
file_size: 6455007
relation: main_file
file_date_updated: 2020-07-14T12:45:08Z
has_accepted_license: '1'
intvolume: ' 17'
issue: '10'
language:
- iso: eng
month: '10'
oa: 1
oa_version: Published Version
page: 7266 - 7297
project:
- _id: 25681D80-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '291734'
name: International IST Postdoc Fellowship Programme
publication: Entropy
publication_status: published
publisher: MDPI
publist_id: '5495'
pubrep_id: '464'
quality_controlled: '1'
scopus_import: '1'
status: public
title: Quantifying emergent behavior of autonomous robots
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
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...