On the Role of Embodiment for Self-Organizing Robots: Behavior As Broken Symmetry

Der, Ralf

doi:10.1007/978-3-642-53734-9_7

Cited by 8 publications

(11 citation statements)

References 36 publications

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“…Tracing such advances is especially important because different champion teams usually employ different approaches, often achieving a high degree of specialisation in a sub-field of AI, for example, automated hierarchical planning developed by WrightEagle [23,24,26,21,28], opponent modelling studied by HELIOS [27], and human-based evolutionary computation adopted by Gliders [11,12]. Many more research areas are likely to contribute towards improving the League, and several general research directions are recognised as particularly promising: nature-inspired collective intelligence [29,30,31], embodied intelligence [32,33,34,35], information theory of distributed cognitive systems [36,37,38,39,40,41], guided self-organisation [42,43,44], and deep learning [45,46,47].…”

Section: Resultsmentioning

confidence: 99%

RoboCup 2D Soccer Simulation League: Evaluation Challenges

Prokopenko

Marian²,

Bai

et al. 2018

RoboCup 2017: Robot World Cup XXI

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Section: Resultsmentioning

confidence: 99%

RoboCup 2D Soccer Simulation League: Evaluation Challenges

Prokopenko

Marian²,

Bai

et al. 2018

RoboCup 2017: Robot World Cup XXI

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“…The approach advocated in the paper not only illustrates the guided self-organisation of a specific embodied system, but also highlights a methodological perspective on the research field: guidance and self-organisation within a dynamic system may be combined through a proper coupling of the behavioral primitives with (selectable) attractors, setting suitable levels of noise and appropriately expressing current goals via the sensory feedback function. This research perspective is well aligned with the view on GSO developed at the intersection of the theory of dynamical systems and machine learning [49][50][51][52][53][54][55][56]: in order to guide a dynamical system, one may restrict its flow to a certain region in phase space, allowing for an otherwise unrestricted development within this bounded area of phase space [57].…”

Section: Special Issuementioning

confidence: 94%

Entropy Methods in Guided Self-Organisation

Prokopenko

Gershenson

2014

Entropy

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Self-organisation occurs in natural phenomena when a spontaneous increase in order is produced by the interactions of elements of a complex system. Thermodynamically, this increase must be offset by production of entropy which, broadly speaking, can be understood as a decrease in order. Ideally, self-organisation can be used to guide the system towards a desired regime or state, while "exporting" the entropy to the system's exterior. Thus, Guided Self-Organisation (GSO) attempts to harness the order-inducing potential of self-organisation for specific purposes. Not surprisingly, general methods developed to study entropy can also be applied to guided self-organisation. This special issue covers a broad diversity of GSO approaches which can be classified in three categories: information theory, intelligent agents, and collective behavior. The proposals make another step towards a unifying theory of GSO which promises to impact numerous research fields.

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“…Then, the system dynamics is split into individual, decoupled feedback loops. As discussed in Der and Martius (2012) and Der (2014) and others, if h = 0 and the coupling strength c is overcritical, each of these loops has two FPs. Considering only the six vertical shoulder joints, the system has 2 6 FPs corresponding to each joint angle either high or low.…”

Section: Discovering New Control Paradigmsmentioning

confidence: 99%

“…As an explanation, we note that, given the friction and elasticity of the ground, the different degrees of freedom strongly interact by the forces exerted on each other when moving on the ground. More details on this so-called physical cross-talk effect may be found in Der (2014). This physical cross-talk has an immediate influence on the velocities of the sensor values, which feeds back to the behavior via the C matrix in a synchronizing way.…”

Section: The Constitutive Role Of the Agent-environment Couplingmentioning

confidence: 99%

“…These symmetries are approximate since they can be perturbed by non-linearities, the actions of the controller, and the interaction with the environment and/or other body parts. Yet, starting from an unspecific dynamics germ, we observe the emergence of motion patterns reflecting the original symmetries of the physical system to a high degree [the principle of parsimonious symmetry breaking, see Der and Martius (2013) and Der (2014)]. …”

Section: Symmetries Of the Physical Dynamicsmentioning

confidence: 99%

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In Search for the Neural Mechanisms of Individual Development: Behavior-Driven Differential Hebbian Learning

Der

2016

Front. Robot. AI

Self Cite

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When Donald Hebb published his 1949 book "The Organization of Behavior" he opened a new way of thinking in theoretical neuroscience that, in retrospective, is very close to contemporary ideas in self-organization. His metaphor of "wiring" together what "fires together" matches very closely the common paradigm that global organization can derive from simple local rules. While ingenious at his time and inspiring the research over decades, the results still fall short of the expectations. For instance, unsupervised as they are, such neural mechanisms should be able to explain and realize the self-organized acquisition of sensorimotor competencies. This paper proposes a new synaptic law that replaces Hebb's original metaphor by that of "chaining together" what "changes together." Starting from differential Hebbian learning, the new rule grounds the behavior of the agent directly in the internal synaptic dynamics. Therefore, one may call this a behavior-driven synaptic plasticity. Neurorobotics is an ideal testing ground for this new, unsupervised learning rule. This paper focuses on the close coupling between body, control, and environment in challenging physical settings. The examples demonstrate how the new synaptic mechanism induces a self-determined "search and converge" strategy in behavior space, generating spontaneously a variety of sensorimotor competencies. The emerging behavior patterns are qualified by involving body and environment in an irreducible conjunction with the internal mechanism. The results may not only be of immediate interest for the further development of embodied intelligence. They also offer a new view on the role of self-learning processes in natural evolution and in the brain. Videos and further details may be found under http://robot.informatik.uni-leipzig.de/research/ supplementary/NeuroAutonomy/.

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On the Role of Embodiment for Self-Organizing Robots: Behavior As Broken Symmetry

Cited by 8 publications

References 36 publications

RoboCup 2D Soccer Simulation League: Evaluation Challenges

RoboCup 2D Soccer Simulation League: Evaluation Challenges

Entropy Methods in Guided Self-Organisation

In Search for the Neural Mechanisms of Individual Development: Behavior-Driven Differential Hebbian Learning

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