7-8 May 2018 Gothenburg, Sweden

Robert Lowe & Gordana Dodig-Crnkovic, organizers

Chalmers University of Technology and University of Gothenburg

Foundations of Cyber-Physical Computation

Cyber-Physical Systems integrate computation, networking, and physical processes. They are already being developed as a combination of the existing technologies, often as continuation of embedded systems approaches. However, the concept offers much more possibilities that can be developed by transcending the limitations and drawbacks of the present day computer technologies such as energy and material consumption issues, lack of resilience, speed, and smooth adaptation to variety of new fields like life sciences and medicine, nano-technology and bioinformatics as well as new production and transportation technologies. New computational approaches can also make technological systems less rigid and more human-friendly, such as in the field of soft-robotics and cognitive computing. In order to address these new challenges, there is a need of new unconventional approaches to computing such as physical computing, natural computing, embodied cognitive computing and morphological computing (where morphology of a physical systems computes or processes information or in the case of robotics morphology is used for learning and bodily control).

The symposium will bring together researchers from both theory-driven and application-driven emerging approaches to computation and embodiment who will discuss theoretical positions concerning morphological and embodied computation as well as practical applications and foundations for new technologies.

Morphological computing, at its core, entails that the morphology (shape + material properties) of an agent (physical system, a living organism or a machine) both enables and constrains its possible physical and social interactions with the environment as well as its development, including its growth and reconfiguration. The role of such computation within cognitive systems includes the off-loading of control onto the body and its interaction with the environment thus enabling flexible and adaptive behavior.

So-called embodied cognition holds that cognition is grounded in environmental interactions in the world and is invisible in classical symbolic representation accounts of cognitive function, which is modeled on human “thinking” or “mentality”. However, modern computational perspectives on cognition such as natural computation (including info-computation) account for embodiment whereby cognitive processes are considered to emerge from interactions in the world.

Articles from the workshop will be published in the journal Entropy and Springer SAPERE series.

Invited speakers

Morphological and Embodied Computation Theory

Aaron Sloman University of Birmingham, UK

Lorenzo Magnani, University of Pavia, Italy

Vincent Müller, Anatolia College/ACT, Thessaloniki, Greece and University of Leeds, UK

Marcin Schroeder, Akita International University, Japan

Marcin Milkowski, Polish Academy of Sciences, Warsaw, Poland

Mario Villalobos, University of Tarapacá, Arica, Chile

Przemysław Nowakowski, CPR, Warsaw, Poland

Matej Hoffmann, Czech Technical University in Prague, Czech Republic

Morphological and Embodied Computation Applications

Jordi Vallverdu Autonomous University of Barcelona, Spain

Yulia Sandamirskaya The Institute of Neuroinformatics, University of Zurich and ETH Zurich, Switzerland

Christian Balkenius, Lund University Sweden

Gregor Schöner, Institut für Neuroinformatik, Ruhr-Universität Bochum, Germany

Philipp Schwartenbeck, University College London, UK

Stefan Wermter, University of Hamburg, Germany

Patrizio Pelliccione, Chalmers University of Technology and University of Gothenburg, Sweden

Karen Quigley, Northeastern University, Boston, Massachusetts

Symposium organizers

Robert Lowe, University of Gothenburg & Gordana Dodig-Crnkovic, Chalmers University of Technology, Sweden


Place: Chalmers Conference Center, Catella

Day 1, May 7th: Morphological and Embodied Computation Theory

0900-0920: Coffee

0920-0930: Introduction to Day 1

Robert Lowe & Gordana Dodig-Crnkovic

Session 1

Chair: Vincent Müller

0930-1000: Lorenzo Magnani, University of Pavia, Italy

1000-1030: Marcin Schroeder, Akita International University, Japan

1030-1100: Coffee

Chair: Lorenzo Magnani

1100-1130: Przemysław Nowakowski, CPR, Warsaw, Poland

1130-1200: Vincent Müller, Anatolia College/ACT, Thessaloniki, Greece and University of Leeds, UK

1200-1230: PANEL DISCUSSION 1.

Panel Chair: Marcin Schroeder

1230-1330: Lunch

Session 2

Chair: Mario Villalobos

1330-1400: Marcin Milkowski, Polish Academy of Sciences, Warsaw, Poland

1400-1430: Philipp Schwartenbeck, University College London, UK

1430-1500: Coffee

Chair: Marcin Milkowski

1500-1530: Mario Villalobos, University of Tarapacá, Arica, Chile

1530-1600: Aaron Sloman, University of Birmingham, UK


Panel chair: Aaron Sloman

1630-1700: Summary

Robert Lowe & Gordana Dodig-Crnkovic

1715 Dinner

Day 2, May 8th: Morphological and Embodied Computing Applications

0900-0920: Coffee

0920-0930: Introduction to Day 2 – Robert Lowe & Gordana Dodig Crnkovic

Session 3

Chair: Jordi Vallverdu

0930-1000: Patrizio Pelliccione, Chalmers University of Technology and University of Gothenburg, Sweden

1000-1030: Matej Hoffman, Czech Technical University in Prague, Czech Republic

1030-1100: Coffee

Chair: Patrizio Pelliccione

1100-1130: Jordi Vallverdu, Autonomous University of Barcelona, Spain

1130-1200: Karen Quigley, Northeastern University, Boston, USA.


Panel chair: Matej Hoffman

1230-1330: Lunch

Session 4

Chair: Christian Balkenius

1330-1400: Gregor Schöner, Institut für Neuroinformatik, Ruhr-Universität Bochum, German

1400-1430: Yulia Sandamirskaya, University of Zurich and ETH Zurich, Switzerland

1430-1500: Stefan Wermter, University of Hamburg, Germany

1500-1530: Coffee

Chair: Gregor Schöner

1530-1600: Christian Balkenius, Lund University, Sweden


Panel Chair: Stefan Wermter

1630-1700: Closing

Robert Lowe & Gordana Dodig-Crnkovic



Aaron Sloman: Why increasingly complex embodied products of biological evolution need increasingly complex, increasingly disembodied, cognition, including ancient forms of mathematical cognition

Abstract: The earliest, simplest, organisms acquired information only about their immediate environment and internal states, and had relatively few options for control, e.g. control of osmotic pressure, temperature, inward and outward flow of chemicals, growth, and in some cases motion.[Ganti,2003] Over time, some of the lineages of biological evolution become more complex, with multiple increasingly complex sensors, increasingly complex movable components and force generators, larger amounts of growth and development between conception and maturity, and expanding needs to be able to select paths and actions on increasingly large spatial and temporal scales, to meet increasingly complex and varied needs, using increasingly complex and varied spatially and temporally distributed resources.

These evolutionary and developmental changes increase the need for, and opportunities for *offline*, processing of information, including formation or modification of new concepts/categories, theories, maps, models, explanations, etc., mechanisms for creating, comparing, debugging and manipulating them and formation and use of increasingly complex preferences, motive generators, motive comparators, incomplete plans, etc...

Lorenzo Magnani: Cognitive Domestication of Ignorant Bodies through Computation

Abstract: What I have called eco-cognitive computationalism sees computation as happening in physical entities that are suitably altered so that data can be encoded and decoded to obtain the desired outcomes. Following Turing (1948) it is useful to consider the evolutionary emergence in human animals of information, meaning, and of the first rudimentary forms of cognition, as the product of an interaction and concurrent coevolution, in time, of the states of brain/mind, body, and external environment. At the same time Turing is able to demonstrate that thanks to an imitation of the above process it is possible to invent the new conceptual basis for the subsequent creation of the Universal Practical Computing Machine. Taking advantage of Turing’s viewpoint I will contend that the Universal Practical Computing Machine is that computer that constitutes what I call a mimetic mind. Following this line of thought we can see the processes that gave rise to various forms of computation as forms of cognitive domestication of “ignorant” physical entities: a perspective that sheds new epistemological light on the recent computational importance of the simplification of cognitive and motor tasks generated in organic agents by morphological aspects. A solution that causes in robotics not only new forms of “computational mimesis” of the associated cognitive routines - when feasible - but also the building of amazing mimetic bodies which favor a simplification of the correlated computation, so following the general principle of “simplexity” of animal embodied cognition.

Keywords: Mimetic minds, mimetic bodies, ignorance bodies, eco-cognitive computationalism, morphology.


Turing, A.M., Intelligent machinery [1948]. In: Meltzer, B., Michie, D. (eds.) Machine Intelligence, vol. 5, pp. 3–23. Edinburgh University Press, Edinburgh (1969)

Vincent Müller: Morphological X.

Abstract: A large variety of biological findings as well as experiments in artificial systems show that functions that would usually be attributed to the brain are outsourced to body-environment dynamics. The reduction of computation in the brain as a result of an exploitation of the body has lead to the term "Morphological Computation", a term that has gained a lot of attraction in biology and soft robotics. We believe that it is time to critically analyse the concept in light of recent developments in biology, robotics, and philosophy. This paper has two goals. First, we present an overview over different forms of how body-environment dynamics contribute to cognitive processes. Second, we critically discuss the term Morphological Computation. We believe that is an important step towards developing a unifying perspective on role of morphology in intelligent systems. It seems that "Morphological Computation" does not capture the variety of processes, which is why we titled this paper Morphological X.

Marcin Schroeder: Morphology of Information for the Study of Morphological Computing

Abstract: Qualification of the term “computing” by the adjective “morphological” modifies the seemingly familiar concept of computing in the direction of generalization and generalizations are always journeys into terra incognita. “Morphological” in the general sense refers to the structure or form, therefore we have a compass directing us in this journey. However, not only there are multiple ways leading in the same direction, even the entry point, concept of computing, is not unique. We have the model of computing given by a Turing machine with many equivalent definitions of its components and functioning. Yet, we have also forms of computing which are not equivalent and in some instances very different, for example those which involve analog information. Moreover the distinction between analog and digital information is very often a source of confusion which is sufficiently relevant and important to be discussed more extensively.

This paper has as its main objective clarification of the meaning and role of morphology in the context of computation and information. At first sight the task may seem easy. “Morphological computing” is sometimes described as computing involving shape as a carrier of information. However, the concept of shape, although very intuitive and often used in the common sense discourse, is actually very difficult to define and to systematize. The word “shape” in topological shape theory initiated and developed by K. Borsuk (Concerning homotopy properties of compacta, 1968) is misleading, as it is not concerned with anything which can be expected from the intuitive meaning of the word “shape”. For instance, Warsaw circle is shape equivalent to a circle, but its appearance is of a circle after very serious accident in which it was bent infinitely many times. Shape theory developed by D. G. Kendall was intended as a tool for numerical characterization and statistical analysis of shape understood in the common sense way: “We here define ‘shape’ informally as all the geometrical information that remains when location, scale and rotational effects are filtered out from an object” (Kendall, Shape manifolds, Procrustean Metrics, and Complex Projective Spaces, 1984). It is however disputable whether this theory can actually meet all expectations of the theory of shape in all contexts. For instance, the fact that the object whose shape is considered is reduced to the finite number of quite arbitrary “landmark points” and that the shape is an invariant of rotations around some axis makes it questionable that this approach grasps the essential features of the concept of shape.

Whether these objections are justified or not, it is quite clear that the reduction of morphology to the analysis of shape would not make understanding of morphology easier or more useful. We can only observe that the introductory explanation of Kendall’s concept refers to shape as “geometrical information”. Whatever understanding would be of morphological computing or computing in general, it is a legitimate assumption that they refer to information in its dynamical aspect. Therefore any attempt to characterize morphological computing requires prior study of the relationship between morphology and information. Morphology as a study of organic forms was initiated by J. W. Goethe and independently by C. F. Burdach at the turn of 18th and 19th century. Goethe defined it (On Morphology, 1817) as the science of the form (Gestalt), formation (Buildnung) and transformation (Umbildung) of organic beings. It is important that he conceived it as a study of the form and at the same time as a study of its changes. The tradition of the study of organic forms in terms of their transformations initiated by Goethe became the central paradigm of biology reinforced and redirected by the influential work of D’A. W. Thompson (On Growth and Form, 1917). This direction of inquiry entered the context of information and computing indirectly in the work of A. Turing (The Chemical Basis of Morphogenesis, 1952) and directly and explicitly in the celebrated book of R. Thom (Structural Stability and Morphogenesis, 1972). Thom clearly presents his book as a study of information alternative and superior to the approach of E. C. Shannon.

However, the origins of ideas expressed in the current morphological study of information are much older than the term “morphology” or “computing”. Probably the most influential among the early studies of morphology of information predating these terms was the work of G. W. Leibniz (e.g. On the Art of Combination, 1690). His characteristica universalis is a language based on morphological information directly reflecting thinking without mediation of alphabet and his calculus ratiocinator operating on characteristica universalis is a process of computing. Leibniz wrote in the letter to Nicolas Redmond in 1714 that the integration of science, mathematics and metaphysics through the use of characteristica universalis requires “a kind of general algebra in which all truths of reason would be reduced to a kind of calculus.” He did not understood it as a simple extension of arithmetic. In earlier work (On Universal Synthesis and Analysis, 1679) Leibniz presented calculus ratiocinator as “that science in which are treated the forms or formulas of things in general, that is, quality in general”.

Leibniz did not know that a sub-discipline of mathematics called “general algebra” would be developed. He did not give a clear view of what he expected. This paper ends with a presentation of the earlier published by the author proposal of the general algebra based formalism for information and computation in terms of closure spaces with its characteristica universalis and its calculus ratiocinator. Moreover, this approach allows reconciliation of Thom’s view of information as form with Shannon’s analysis of information based on probability of selection.

Marcin Milkowski: Troubles with the usability constraint on concrete computation

Abstract: In a number of publications, Gualtiero Piccinini defends the view that any concrete computation must be usable for a biological agent (Piccinini 2011, 2015): “for a physical process to count as a computation (and thus for it to be relevant to Physical CT properly so called), it must be usable by a finite observer to obtain the desired values of a function using a process that is executable, automatic, uniform, and reliable”. However, this constraint is problematic for two reasons. First, the finite observer needs to interpret the output of the process as meaningful. This means that the account of computation presupposes the account of semantic information, which is avowedly rejected by defenders of the mechanistic approach to concrete computation. Second, although other theorists also upheld this principle (Maroney and Timpson 2017), it is at best problematic when associated with immediate control. For example, an active homing nuclear missile may use a computational process to find the target but no biological observer will be able to read the value because all will be killed. However, it seems deeply counterintuitive to call such a homing process non-computational if it is guided by a standard computer.

I will present an alternative account of the usability constraint that abstracts away from finite observers of the computation, and posed in terms of available causal-informational generalizations, developing the points stated in (Miłkowski 2013). However, this account comes at a certain cost: it cannot be used to exclude some forms of physical processes as non-computational, and when made more stringent, it may be actually equivalent to a sophisticated version of the usability constraint.


Maroney, O. J. E., & Timpson, C. G. (2017, April 6). How is there a Physics of Information? On characterising physical evolution as information processing. Preprint. Accessed 7 November 2017

Miłkowski, M. (2013). Explaining the Computational Mind. Cambridge, Mass.: MIT Press.

Piccinini, G. (2011). The Physical Church-Turing Thesis: Modest or Bold? The British Journal for the Philosophy of Science, 62(4), 733–769. doi:10.1093/bjps/axr016

Piccinini, G. (2015). Physical computation: a mechanistic account. Oxford: Oxford University Press.

Mario Villalobos: Computation and cognition: A (new) enactive reading

Abstract: Historically, the enactive approach to cognition was borne and developed in strong opposition to the computational theory of mind. More contemporary enactivism, at least in its autonomist and radical versions, seems to hold this anti-computationalist stance as a nonnegotiable theoretical flag. Why is this so? Cognition, according to the enactive view, is something that only autonomous systems can display. Autonomous systems, considered as cognitive systems, do not represent a pre-given reality but bring forth (enact) their own world of significance. Computational systems, so the enactive argument goes, are not autonomous systems and consequently cannot display genuine cognition (i.e., enaction). In this talk, I assess the anti-computational stance of enactivism and argue that it is ill-founded in several respects. The kind of autonomy that enactivism sees as a mark of cognition is not precluded, so I argue, for computational systems. If successful, the analysis might relax the conceptual war between enactivism and computationalism and open some paths to their integration.

Przemysław Nowakowski: Does the body actually make a difference?

Abstract: In the literature related to morphological computation, we can find a debate about tradeoffs between central and peripheral processing (Caluwaerts et al. 2013; Ghazi-Zahedi et al. 2017; Nowakowski 2017). Here, I want to focus more on the peripheral/bodily part of this tradeoff. According to Shapiro (2004), different body types determine different minds or different cognitive abilities. Wilson and Foglia (2011/2016) expand this account and propose that bodies constrain, distribute, and regulate cognitive processing, therefore we can assume that different bodies should do this job in different ways. Here, I assume that if the computational approach to cognition is correct, and if body really makes a difference, different bodies should determine different computations underlying cognition. But does the body really can make such a difference?

Based on literature on causal specificity (Calcott et al. 2017; Griffiths et al. 2015; Woodward 2010) and comparative and evolutionary studies on perceptual systems (Parker 2003; Lazareva et al. 2012; Cronin et al. 2014), I will search an answer to the question expressed in the title and present it in the context of the recent work on morphological computation.


Calcott, B., Griffiths, P. E., & Pocheville, A. (2017). Signals that make a difference. The British Journal for the Philosophy of Science, axx022.

Camhi, J. M. (1984). Neuroethology: Nerve cells and the natural behavior of animals. Sinauer Associates Inc.

Caluwaerts, K., D'Haene, M., Verstraeten, D., & Schrauwen, B. (2013). Locomotion without a brain: physical reservoir computing in tensegrity structures. Artificial life, 19(1), 35-66.

Cronin, T. W., Johnsen, S., Marshall, N. J., & Warrant, E. J. (2014). Visual ecology. Princeton University Press.

Ghazi-Zahedi, K., Langer, C., & Ay, N. (2017). Morphological computation: Synergy of body and brain. Entropy, 19(9), 456.

Griffiths, P. E., Pocheville, A., Calcott, B., Stotz, K., Kim, H., & Knight, R. (2015). Measuring causal specificity. Philosophy of science, 82(4), 529-555.

Lazareva, O. F., Shimizu, T., & Wasserman, E. A. (Eds.). (2012). How animals see the world: Comparative behavior, biology, and evolution of vision. Oxford University Press.

Parker, A. (2003). In the blink of an eye: how vision sparked the big bang of evolution. New York: Basic Books.

Nowakowski, P. R. (2017). Bodily processing: the role of morphological computation. Entropy, 19(7), 295.

Shapiro, L. (2004) The Mind Incarnate; MIT Press: Cambridge, MA, USA.

Wilson, R.A.; Foglia, L. (2011/2016) Embodied Cognition. In The Stanford Encyclopedia of Philosophy; Zalta, E.N., Ed.; Metaphysics Research Lab, Stanford University: Stanford, CA, USA, 2016; Available online: (accessed on 19 June 2017).

Woodward, J. (2010). Causation in biology: stability, specificity, and the choice of levels of explanation. Biology & Philosophy, 25(3), 287-318.

Matej Hoffmann: Exploiting body morphology in animals and robots: control, perception, but rarely computation

Abstract: The contribution of the body in natural and artificial agents is increasingly described as "off-loading computation from the brain to the body", where the body is said to perform "morphological computation". I will argue that this contribution is rarely truly computational and I will distinguish: (1) morphology that facilitates control, (2) morphology that facilitates perception, and the rare cases of (3) morphological computation proper, such as "reservoir computing". For engineers, the advances in new materials (e.g., soft, deformable, compliant) pave the way for robots with completely new capabilities. However, the exploitation of this potential is not straightforward and often at odds with classical engineering approaches to modeling and control. Thus, I will look at the pros and cons of simple vs. complex bodies, critically reviewing the attractive notion of "soft" bodies automatically taking over control tasks. Finally, I will address another key dimension of the design space—whether model-based control should be used and to what extent it is feasible to develop faithful models for different morphologies.


Jordi Vallverdu: How to Bias Hyperheuristics Through Bioinspiration

Abstract: One of the classic problems of understanding general knowledge has been multimodal data binding. But once it has been achieved, or at least partially solved, AI faces now a similar problem that also affects humans: how to combine different heuristics in dynamic scenarios with several action choices. That is, how to manage hyperheuristically between different reasoning models, strategies, or mechanisms. Possible bioinspired strategies can offer ways of dealing this unconventional computing process, such as emotional architectures or even physically influenced processors like memristors. The skill of combining or selecting among a list of diverse set of heuristics can contribute to “weaken” or “bias” AI, but at the same time this skill can provide powerful adaptive behaviors.

Yulia Sandamirskaya: Neuromorphic Cognitive Agents

Abstract: In this talk, I will present the state of the art in neuromorphic computing and sensing technology. In particular I will explain how dynamics of spiking neural networks can be emulated using mixed-signal analog/digital electronic circuits. Furthermore, I will demonstrate how the neural-dynamic framework of dynamic neural fields can be used to wire-up and configure these devices to build neuromorphic cognitive architectures. I will show how these neuronal architectures can generate robotic behavior and learn from experience in a simultaneous localisation and mapping task — a seminal task, in which a robotic agent learns and updates a representation of an unknown environment, based solely on its own sensory input and motor commands. The map of the environment is generated and stored in plastic synapses on the neuromorphic device, while the robot’s behavior is generated and its trajectory is tracked by activity of artificial spiking neurons. This work shows how computing can be done without a digital computer in the loop, but solely using neuronal dynamics, coupled to sensors and motors of a behaving agent.

Christian Balkenius: Dynamic coupling of the internal and external world: a memory system for a robot

Abstract: I will describe a memory model for robots that can account for many aspects of an inner world, ranging from object permanence, episodic memory and planning to imagination and reveries. It is modeled after neurophysiological data and includes parts of the cerebral cortex together with models of emotion and arousal systems. The three central components are an identification network, a localization network and a working memory network.

A central idea of the model is that the internal memory system and the external world are seen as two dynamical systems that can be coupled or decoupled depending on the type of processing that is required. Attention serves as the interface between the two systems. It directs the flow of information from sensory organs to memory, as well as controlling top-down influences on perception and action.

The model has been tested in a number of computer simulations that illustrate how it can operate as a component in various cognitive tasks including perception, the A-not-B test, delayed matching to sample, episodic recall and vicarious trial-and-error.

Gregor Schöner: Embodied cognition does not necessarily engage the body, but cognitive processes share properties with sensory-motor processes

Abstract: The central nervous system evolved as a perception and action machine. The origins of cognition in the sensory-motor domain are evident in development as well. Sensory-motor processes are continuous in state and in time and may be linked to online sensory inputs. I will argue what I call the strong embodiment hypothesis: All cognitive processes share properties with sensory-motor processes, in particular, dynamic stability and the capacity to link to sensory and motor surfaces. This does not imply at all that cognition is necessarily accompanied by motoric or sensory processing. But it has far reaching implications for the inner structure of cognition that I will lay out. In particular, I will show how the embedding of concepts in low-dimensional feature spaces emphasized by Gärdenfors is consistent with the grounding of cognition in the representations that are ultimately sensory-motor in nature. Cognition entails coordinate transforms or “steerable” maps” that make it possible to link to sensory-motor representations while also generating the kind of invariances that characterize cognition. I will discuss how instabilities solve the central problem of strong embodiment, how sequences of processing steps may arise from a time and state continuous substrate. The grounding of spatial relations and action concepts will serve to illustrate these ideas.

Philipp Schwartenbeck: Predictive Processing in planning and choice behaviour based on active Bayesian inference

Abstract: Predictive processing has recently become one of the dominant theoretical accounts of cognition and adaptive behaviour. A central idea of predictive processing is that to behave successfully, agents need to build a (generative) model of their environment and perform adaptive inference and learning with respect to this model. I will present a theoretical account that applies the ideas of predictive processing to choice behaviour and casts decision-making and planning as active Bayesian inference. In active inference, agents are assumed to form prior expectations about future outcomes and minimise information-theoretic surprise about these outcomes by inferring policies that fulfil these expectations, i.e. make it likely to attain expected states. This perspective highlights the role of information theory and Bayesian inference in cognition, and provides a candidate account for how these processes might be implemented in brain function based on variational message passing.

I will provide a general and intuitive introduction into the computational architecture of active inference. Further, I will discuss interesting predictions arising from this perspective. For example, active inference applies the same (information-theoretic) currency to maximising reward and gaining insight about the structure of the world. This provides a candidate mechanism for an implicit trade-off between information-gain (exploration) and maximising reward (exploitation), and predicts that behaviour will be guided by both information-seeking and goal-directed pragmatic behaviour. Further, active inference predicts a central involvement of neuromodulators, particularly dopamine, in inferring beliefs about policies and states of the world. I will present theoretical and empirical evidence for these predictions, and also highlight important open questions, such as how agents infer task representations that provide the basis for adaptive inference and learning.

Stefan Wermter: Neural Models for Crossmodal Learning and Embodied Computing

Abstract: Neural learning approaches and human-robot interaction are often addressed in different research communities. In this talk I will describe neural network models with a particular focus on crossmodal learning for human-robot interaction. Our goal is to better understand embodied learned communication in humans and machines and to use the knowledge we gain to improve multisensory integration and social interaction in humanoid domestic robots. In the context of understanding grounded communication, we will present deep neural network models for emotion expression recognition for human-robot interaction. Furthermore, we will present a model using self-organizing neural networks for human-action recognition in the context of human-robot assistance. We argue that continual neural learning of emotion perception, multisensory integration and observation of human actions are essential for future human-robot cooperation.

Patrizio Pelliccione: The revolution of software in the robotic domain

Abstract: Software is becoming increasingly important in many domains and it is eating many traditional businesses. The robotic domain is not an exception. Robotics promises to provide solutions that will support and replace humans for a broad variety of tasks and in a huge variety of contexts; examples are service, industrial, military, or space robots. However, the main focus of researchers and practitioners so far has been on providing tailored software and hardware solutions for very specific and often complex tasks. Deploying even simple applications requires integrating solutions from experts of various domains, including navigation, path planning, manipulation, localization, human-robot interaction, etc. This is why robotics is one of the most challenging domains for software engineering.

While analysing the aspects and challenges above, in the talk I will try to establish a connection between software engineering and morphological computing with the aim to bring at the event a different perspective coming from the software engineering field. The talk will provide examples and insights coming from a H2020 EU project in robotics, called Co4Robots.

Karen Quigley: Allostasis and Interoception: Why it Matters that a Brain Operates Inside a Body

Abstract: For much of the history of psychology, emotion, cognition, motor function, sensation and perception, and the functions of the visceral organs in the body’s periphery (i.e., physiology) were studied as if they were separate human faculties -- they are not. Researchers are beginning to recognize that, like the brain, the periphery of the body has a critical role in the overall functioning and well-being of humans. I will discuss how two phenomena, allostasis and interoception, link the activities of the human body and brain. Allostasis constitutes the physiological regulatory processes that balance the continual acquisition and utilization of biological resources required to support major organismic goals such as growth, reproduction and survival. Allostasis is the major task of a brain, and an optimally efficient brain anticipates changes in the world outside the body (and inside it), more than it ‘reacts’ to them. Interoception comprises the sensations from the body that indicate the state of the body’s periphery. Interoceptive signals provide critical information to the brain about the current environment outside the body including sensing the current state of the body both of which are important for supporting allostasis. Together, allostasis (i.e., processes for energy regulation) and interoception (i.e., sensing what is happening outside the skull) provide key organism-level functions that optimally enable the maintenance of life. These are also key processes underlying emotion and cognition, which by biological necessity, are embodied.


Hotel Location

The hotel is located near the central station from where you will have taxis ready waiting for you (with name tags).

The hotel is centrally located, about 5-10 minutes’ walk from the popular Avenyn area. On Monday morning (0800) Robert will meet you at the hotel reception and help you buy bus tickets (across the road) and travel with you on the tram to the symposium venue. Breakfast at the hotel is paid for by us (the Marcus Wallenberg grant).

Symposium venue:

The symposium will be held at the Chalmers Johanneberg campus (conference room Catella) located in central Gothenburg. Shows Chalmers Johanneberg inside view Shows Catella room on the plan

Dinner/Lunches venue:

Lunches (Monday 7th May, Tuesday 8th May) and symposium dinner (Monday 7th May) will be held at Hyllan restaurant, Chalmers,.

Supported by:

Marcus Wallenberg Foundation for International Scientific Collaboration &

Chalmers Area of Advance ICT &

Department of Applied Information Technology, University of Gothenburg