Conceptual and Mathematical Foundations of Embodied Intelligence

Abstracts of the posters

Alexandra Barchunova
Bielefeld University, Germany
(joint work with Jan Moringen, Robert Haschke, Helge Ritter)

Embodied intelligence within manual interaction: multimodality, decomposition, recognition

For humans, manual interaction with the surrounding objects and its recognition is an essential cognitive ability, significant for survival. When we observe, how others interact with objects, we usually see continuous movements of the fingers accompanied in some cases by an acoustic noise. When we carry out a joint manual action, e.g. moving furniture, we also sense the pressure caused by force application of the interaction partners. Nevertheless, we are capable of integrating different sensory modalities, splitting the continuous low-level observations into chunks and assigning them to semantic categories, such as "grasping", "holding", "pouring", "cutting", or "shaking".

Motivated by the latest psychological and neuroscientific findings, in our work we pursue decomposition and recognition for multimodal bi-manual time series on a semantic level. The conceptual basis of our work is inspired by the Activity Theory presenting interaction on three levels of complexity: action primitives, actions and activities. Following the hotly debated question of identification of action primitives, we propose a two-stage approach.

In the first stage, inspired by the findings of Hemeren and Thill (2011) we conduct a decomposition of interaction into action primitives based on detection of change in multimodal data. To this end, we present the first application of a Bayesian algorithm for multiple change detection introduced by Fearnhead (2006) to decomposition of multimodal interaction time series into action primitives. For this purpose we propose an approach that integrates simple stochastic models (autoregressive, constant and threshold models), representing unimodal segments, to multimodal representation of action primitives. The great advantages of the proposed method are that it neither need any pre-training, nor action-specific template knowledge, nor interaction-specific segmentation heuristics. In the second step, we conduct supervised and unsupervised learning of the resulting action primitive segments based on ordered means models (Großekathöfer and Lingner, 2004).

Within the experimental scenario, the multimodal manual interaction data is represented by the applied force, audio signal and kinematic trajectories of the hand recorded during action execution for both hands. The sequence includes representative actions, such as "grasp", "hold", and "screw". In order to acquire ground truth automatically, in our work we present an alternative method to hand labeling of the observations, the audio-cue schedule.

Altogether, with the proposed method we aspire a generic approach to recognition of interaction, applicable in a wide range of scenarios, and integrating different modalities.

José R. Donoso
Bernstein Center for Computational Neuroscience, Germany

Sensorimotor navigation and the role of embodiment in spatial memory

One of the aims of neuroethology is to pinpoint the neural structures and mechanisms implementing a specific function defined in the behavioral domain. Consistent with a cognitivist paradigm, mainstream research in spatial memory is founded on the idea of the acquisition of a cognitive map; a neural substrate that during exploration encodes the topological properties of the environment for subsequent retrieval. Such representation requires a process that allows other "modules" within the agent to make use of this information in order to plan current and future behavior. However, a biologically plausible read-out mechanism has remained elusive, making it difficult to connect the behavioral nature of spatial navigation with a plausible neural mechanism within a purely cognitivist framework. Here I show how simple principles of embodiment and sensorimotor associations can provide a bridge between the behavioral and biological levels. By means of a simple embodied-connectionist model, I illustrate how these concepts can account for the relatively complex behavior involved in a navigational task. I discuss the limitations of the model and possible extensions that could provide insights into the neural mechanisms underlying spatial memory under the light of up-to-date experimental findings in rodents.

Andrée Ehresmann
Université de Picardie Jules Verne, France

Some properties at the root of embodied intelligence

What are the properties enabling a cognitive system to develop embodied intelligence? The problem is studied using the theory of Memory Evolutive Systems (Ehresmann & Vanbremeersch, 2007). MES give a mathematical model, based on Category Theory, for multi-scale systems with a tangled hierarchy of components varying over time; their dynamic is modulated by a network of internal agents with different rhythms and functions, with the help of a flexible long-term 'memory' allowing for learning and adaptation.
A Neuro-Bio System is represented by a MES which takes account of the different levels of the entire organism and of its biological, neural, cognitive and mental processes. This model points out 3 properties essential for embodied intelligence:

  1. a kind of 'flexible redundancy' (Multiplicity Principle);
  2. Synchronicity Laws to be respected by agents of different levels;
  3. formation of a central "Archetypal Core", which integrates an internal model of the organism and its environment, and acts as a driving force for developing embodied intelligence.

An application is given to construct intelligent cognitive systems, in particular Neuro-Bio-ICT systems where a Neuro-Bio system is coupled with an artificial cognitive system ("Exocortex"), to enhance human capacities by integrating, self-structuring and exploiting multiple sources of information.

  • Ammon (von) R., Ubiquitous Complex Event Processing (U-CEP), Submission to FET/Flagship 2010.
  • Edelman, G.M. The Remembered Present; Basic Books: New York, NY, USA, 1989.
  • Ehresmann, A.C.; von Ammon, R.; Iakovidis, D.K.; Hunter, A. Ubiquitous complex events processing in Exocortex applications and mathematical approaches, 2012.
  • Ehresmann, A.C.; Vanbremeersch, J.-P. Memory Evolutive Systems: Hierarchy, Emergence,Cognition; Elsevier: Amsterdam, The Netherlands, 2007.
  • Hagmann, P.; Cammoun, L.; Gigandet, X.; Meuli, R.; Honey, C.J.; Wedeen, Van J.; Sporns, O. Mapping the Structural Core of Human Cerebral Cortex. PLoS Biol. 2008, 6, 1479-1493.
  • Kan, D.M. Adjoint Functors. Trans. Am. Math. Soc. 1958, 89, 294-329.
  • Mac Lane, S. Categories for the working mathematician; Springer, 1971.

Stefan Leijnen
Radboud University Nijmegen, Netherlands
(joint work with Pim Haselager)

Creativity and constraint in self-structuring systems

Under some definitions, creativity is an intrinsically unformalizable process. Yet, by aiming for a formal description of creativity, we address exactly those difficult problems that seemingly elope the current computational paradigm: the origins of structure, the nature of cognitive embodiment, and the relation between the signal of information and its object.

Creativity is often associated with freedom, unboundedness and the availability of a wide array of choices. Here, creativity is somewhat paradoxically defined as its apparent opposite: a process aimed towards incessantly generating constraints (Leijnen, 2011). In a process of self-limitation through self-organization, semi-stable structures arise, which in turn may affect the very same processes that underlie them (Juarrero, 1991; Gonzalez & Haselager, 2005; Deacon, 2012). In time, a higher-order loop may emerge, in which the system is no longer bounded by these self-limiting processes; rather, these (now creative) processes enable the invention of ever more varied and specialized structures. In this ongoing research project, the steps that build up to this hierarchical logic are analyzed, described, and will ultimately be formalized. Importantly - with respect to the embodiment paradigm - this approach allows informational concepts to arise up from physical constraints, and thereby forms an explanation for how cognition may come about, rather than assuming an already in-place structure.

  • Deacon, T.W. (2012). Incomplete Nature: How Mind Emerged from Matter. New York: W. W. Norton and Company, 2012.
  • Gonzalez, M.E.Q. & Haselager, W.F.G. (2005). Creativity: Surprise and abductive reasoning. Semiotica 153, 1/4, 325-341.
  • Juarrero, A.J. (1999). Dynamics in Action: Intentional Behavior as a Complex System. Cambridge: MIT press.
  • Leijnen, S. (2011). Thinking Outside the Box: Creativity in Self-Programming Systems. Workshop on Self-­Programming in AGI Systems. Fourth Conference on Artificial General Intelligence, August 3-7, 2011, Mountain View, CA.

Avinash Ranganath
Universidad Carlos III de Madrid, Spain
(joint work with Luis Moreno Lorente)

Morphomotion: morphology independent locomotion controller for modular robots

A locomotion gait in an animal, which comes about as a result of repetitive and coordinated movement of limbs/joints can be seen as a collection of oscillations, with phase relation between such oscillators determining the emerged gait. Similarly in a modular robotic organism, made up of several independent unit modules with 1 DOF each [1], a variety of locomotion gaits can be achieved by applying simple phase-differed sinusoidal oscillators to unit modules [2]. The phase difference between oscillating modules can either be predetermined [3], or modules could explicitly communicate among each other to converge to an optimal phase-relation [4]. Since a modular robot is an embodied system, made up of physically connected unit modules, there exists inter-modular (or intra-configuration) forces among modules in a given modular robotic configuration, which could be seen as implicit communication among modules. Using these forces, modules in a given configuration can converge and settle into a steady phase difference, resulting in a stable locomotion gait.

We have developed a distributed, homogeneous, adaptive, neural-controller for controlling unit modules, based on implicit inter-modular communication, resulting in stable locomotion gait [5]. The controller parameters are optimised using a Genetic Algorithm, individually for each of the five distinct modular robotic organisms we have experimented with. Adaptability of the controller can be determined by cross-evaluating controllers evolved for each organism on rest of the organisms. Cross-evaluation experiments, in most cases, resulted in stable locomotion gait closely resembling that of the organism's original locomotion gait, implying the influence of an organism's morphology on the emerged behaviour.

  2. Gonzalez-Gomez, J. November 2008. Modular Robotics and Locomotion: Application to Limbless robots. PhD thesis, EPS, UAM, Madrid, Spain.
  3. Gonzalez-Gomez, J., Boemo, E. September 2005. Motion of Minimal Configurations of a Modular Robot: Sinusoidal, Lateral Rolling and Lateral Shift. Proc. of the 8th International Conference on Climbing and Walking Robots, CLAWAR, London. pp. 667-674.
  4. Shen, W.-M., Salemi, B., Will, P. 2002. Hormone-inspired adaptive communication and distributed control for conro self-reconfigurable robots. IEEE Transactions on Robotics and Automation.
  5. A.Ranganath; J.González-Gómez; L.Moreno. Morphology Dependent Distributed Controller for Locomotion in Modular Robots. Proceedings of the Post-Graduate Conference on Robotics and Development of Cognition. Lausanne. Switzerland. Sep., 2012.

Christoph Salge
University of Hertfordshire, United Kingdom
(joint work with Cornelius Glackin and Daniel Polani)

Empowerment and state-dependent noise

Empowerment offers a goal independent utility function based on the embodiment of an agent, and the dynamics of the world the agent is situated in. Recently we demonstrated that Empowerment in the continuous domain can be computed significantly faster if the world dynamics are approximated as multiple, co-dependent, linear Gaussian channels, assuming constant, state-independent Gaussian Noise. Modelling the channel as a more generic Gaussian Process, possibly obtained via a Gaussian Process Learner, now allows us to determine the actual noise levels for a specific state.
This allows new insights into the relationship to other agents, since co-inhabitation of a shared environment implies that several agents have an effect on the same environmental parameters. If the actions of another agent cannot be predicted they become a source of noise, reducing the empowerment. Empowerment maximisation then leads to interesting behaviour, such as avoiding collision with other agents, as the outcome is highly dependent on the other agent’s actions, and therefore is hard to predict.

Nico Schmidt
University of Zurich, Switzerland
(joint work with Matěj Hoffmann, Kohei Nakajima)

Information flow in a quadruped running robot quantified by transfer entropy

Animals and humans engage in an enormous variety of behaviors which are orchestrated through a complex interaction of physical and informational processes. The physical interaction of the bodies with the environment is intimately coupled with informational processes in the animal’s brain. A crucial step toward the mastery of all these behaviors seems to be to understand the flows of information in the sensorimotor networks. In this study, we have performed a quantitative analysis in an artificial agent - a running quadruped robot with multiple sensory modalities - using tools from information theory (transfer entropy and its recently proposed decomposition). Starting from no prior knowledge, through systematic variation of control signals and environment, we show how the agent can discover the structure of its sensorimotor space. We propose that the agent could utilize this knowledge to: (i) drive learning of new behaviors; (ii) identify sensors that are sensitive to environmental changes; (iii) discover a primitive body schema.

Henry Schütze
Universität zu Lübeck, Germany

Visual exploration and predictive information

The autonomous exploration of the environment is a crucial behavioral task of autonomous robots. It has been shown that predictive information (PI) in sensor space is a useful measure in order to assess the quality of exploration behavior. In this work we employ PI to the exploration of static visual scenes, i.e. images. We model autonomous visual exploration by a small region of interest (ROI), which repositions itself in a larger image (the ROI defines the sensor and the repositioning the actuator). In contrast to, e.g., a simple two-wheel embodied robot, we did not assume a specific linear coupling between sensors and actuators. We present two simple behavioral rules, which generate a sequence of sensor values that, in one case, maximize and, in another case, minimize the PI in sensor space. However, both strategies lead to (practically) the same visual exploration behavior: on synthetic and natural images, regions containing rare sensorial configurations like edges and corners are more frequently visited than homogenous regions; a strategy that makes sense since edges and corners are more salient (i.e. they attract human gaze). This result is interesting since, in this scenario, the same behavior is characterized by completely different values of predictive information.

Alexander Terekhov
University of Pierre and Marie Curie, France
(joint work with Kevin O'Regan)

Discovering rigid displacements by a naïve agent

The laws of rigid displacements are the most basic and fundamental characteristics of spatial knowledge. Rigid displacements are implicitly assumed to be known to an agent in the majority of the existing algorithms of self-organization and calibration. But consider a naïve agent that stares at “the blooming buzzing confusion” of its sensory inputs and motor outputs and has no idea about the nature of the information the sensations carry - how can such an agent learn the laws of rigid displacements? In the current poster we give a partial answer to this question. Following Poincaré we assume that the key aspect of rigid displacements is that they represent laws that are shared between objects it perceives and the agent itself. As a consequence the agent can always perform an action that nullifies the changes of the sensory inputs caused by rigid displacements. We simulated an agent performing translational motions in a plane while looking at the starry skies above it. The agent has a retina with a few randomly placed photoreceptors with highly non-local Gaussian tuning curves. The agent can displace the retina without rotations and measure its position with randomly placed proprioceptive neurons, again having non-local Gaussian tuning curves. Without knowing it explicitly, the agent can learn the mappings of proprioception into itself, which in reality correspond to the rigid displacements. Using these mappings the agent can pass the most basic tests of spatial knowledge: (1) when shown two different patterns of stars the agent can say if this is the same pattern or not; (2) when displaced along two multi-segmental paths under different skies the agent can say if the end points of the two paths coincide (assuming that the starting points do). We conclude that the laws of rigid displacements can be learned by a naïve agent and that these laws allow the most basic aspects of spatial knowledge to be manifested.

Wanja Wiese
Johannes Gutenberg-Universität, Germany

What can Friston's free-energy principle tell us about embodied cognition?

Well-known ways in which Friston's free-energy principle is related to embodied cognition (EC) include the propositions (i) that an agent embodies a model of its environment and of its own body as related to that environment [1]; plus (ii) that there is an intimate conceptual connection between action and perception [2]. The poster claims that the principle can contribute to research on EC in a more radical way: As the free-energy principle suggests that information-processing in the brain relies essentially on generative models, asking in which sense such models are representational possesses an even higher relevance for the explanatory force of EC, in particular regarding (i) the disputed need to refer to amodal mental representations, and (ii) the explanatory value of positing representations in general.
In order to establish that a generative model is representational in an interesting sense, it must on the one hand be shown that the content of a generative model can be determined in a way that allows for misrepresentation. Two such ways are provided by structural theories of representational content (as suggested by Andy Clark [3, p. 85]) and refined statistical theories (as proposed by Jakob Hohwy [4, ch. 8]).
On the other hand, it must also be the case that neurally implemented generative models actually fulfill a functional role that renders them representational. The main positive contribution of this poster is to show how the free-energy principle, by positing a hierarchical generative model in the brain, suggests a middle ground between representationalism and anti-representationalism in at least two respects:

  1. Neural populations towards the lower end of the hierarchy fulfill a role that renders them less representational, while populations towards the upper end tend to be representational in a more robust sense.
  2. By emphasizing the role of non-linear coupling between levels of the hierarchy, it is also suggested that any division between tasks that do not require representations and those that do is ultimately untenable.
  1. Friston, K. (2011). Embodied inference: or “I think therefore I am, if I am what I think”. In W. Tschacher & C. Bergomi (eds.), The Implications of Embodiment. Cognition and Communication. Imprint Academic.
  2. Friston, K., Daunizeau, J., Kilner, J., & Kiebel, S. (2010). Action and behavior: a free-energy formulation. Biological Cybernetics. doi: 10.1007/s00422-010-0364-z
  3. Clark, A. (forthcoming). Whatever next? Predictive brains, situated agents, and the future of cognitive science. Behavioral & Brain Sciences.
  4. Hohwy, J. (forthcoming). The Predictive Mind. Oxford: Oxford University Press.


Date and Location

February 27 - March 01, 2013
Max Planck Institute for Mathematics in the Sciences
Inselstraße 22
04103 Leipzig
see travel instructions

Scientific Organizers

  • Nihat Ay, MPI for Mathematics in the Sciences
  • Ralf Der, MPI for Mathematics in the Sciences
  • Keyan Ghazi-Zahedi, MPI for Mathematics in the Sciences
  • Georg Martius, MPI for Mathematics in the Sciences

Administrative Contact

Antje Vandenberg
MPI for Mathematics in the Sciences
Contact by Email

Financial Support

28.06.2018, 08:45