On the Information Theoretic Implications of Embodiment - Principles and Methods

Embodied intelligent systems are naturally subject to physical constraints, such as forces and torques (due to gravity and friction), energy requirements for propulsion, and eventual damage and degeneration. But embodiment implies far more than just a set of limiting physical constraints; it directly supports the selection and processing of information. Here, we focus on an emerging link between information and embodiment, that is, on how embodiment actively supports and promotes intelligent information processing by exploiting the dynamics of the interaction between an embodied system and its environment. In this light the claim that "intelligence requires a body" means that embodied systems actively induce information structure in sensory inputs, hence greatly simplifying the major challenge posed by the need to process huge amounts of information in real time. The structure thus induced crucially depends on the embodied system's morphology and materials. From this perspective, behavior informs and shapes cognition as it is the outcome of the dynamic interplay of physical and information theoretic processes, and not the end result of a control process that can be understood at any single level of analysis. This chapter reviews the recent literature on embodiment, elaborates some of the underlying principles, and shows how robotic systems can be employed to characterize and quantify the notion of information structure.

[1]  Russ Tedrake,et al.  Efficient Bipedal Robots Based on Passive-Dynamic Walkers , 2005, Science.

[2]  G Turkewitz,et al.  Limitations on input as a basis for neural organization and perceptual development: a preliminary theoretical statement. , 1982, Developmental psychobiology.

[3]  Ron Chrisley,et al.  Embodied artificial intelligence , 2003, Artif. Intell..

[4]  A. Noë,et al.  A sensorimotor account of vision and visual consciousness. , 2001, The Behavioral and brain sciences.

[5]  D. Lewkowicz Perception of serial order in infants. , 2004, Developmental science.

[6]  Shimon Edelman,et al.  Representation and recognition in vision , 1999 .

[7]  Mark Wexler,et al.  Depth perception by the active observer , 2005, Trends in Cognitive Sciences.

[8]  H. Barlow The exploitation of regularities in the environment by the brain. , 2001, The Behavioral and brain sciences.

[9]  B. L. Green,et al.  The Adaptive Nature of Cognitive Immaturity. , 1992 .

[10]  Michael P. Kaschak,et al.  Grounding language in action , 2002, Psychonomic bulletin & review.

[11]  Randall D. Beer,et al.  The Dynamics of Active Categorical Perception in an Evolved Model Agent , 2003, Adapt. Behav..

[12]  Randall D. Beer,et al.  The brain has a body: adaptive behavior emerges from interactions of nervous system, body and environment , 1997, Trends in Neurosciences.

[13]  Fumiya Iida,et al.  New Robotics: Design Principles for Intelligent Systems , 2005, Artificial Life.

[14]  Fiona N. Newell,et al.  Visual, haptic and cross-modal recognition of objects and scenes , 2004, Journal of Physiology-Paris.

[15]  E. Gibson,et al.  An Ecological Approach to Perceptual Learning and Development , 2000 .

[16]  Rolf Pfeifer,et al.  How the Body Shapes the Way We Think: A New View of Intelligence (Bradford Books) , 2006 .

[17]  Olaf Sporns,et al.  Methods for quantifying the informational structure of sensory and motor data , 2007, Neuroinformatics.

[18]  G. Lakoff,et al.  Philosophy in the flesh : the embodied mind and its challenge to Western thought , 1999 .

[19]  Thomas P. von Hoff,et al.  Adaptive step-size control in blind source separation , 2002, Neurocomputing.

[20]  Rolf Pfeifer,et al.  Understanding intelligence , 2020, Inequality by Design.

[21]  H. Sebastian Seung,et al.  Stochastic policy gradient reinforcement learning on a simple 3D biped , 2004, 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566).

[22]  I. Gauthier,et al.  Visual object understanding , 2004, Nature Reviews Neuroscience.

[23]  B. Webb,et al.  Can robots make good models of biological behaviour? , 2001, Behavioral and Brain Sciences.

[24]  Henri Cohen,et al.  Handbook of categorization in cognitive science , 2005 .

[25]  M. Goodale,et al.  Active manual control of object views facilitates visual recognition , 1999, Current Biology.

[26]  F. Varela,et al.  Radical embodiment: neural dynamics and consciousness , 2001, Trends in Cognitive Sciences.

[27]  Dana H. Ballard,et al.  Animate Vision , 1991, Artif. Intell..

[28]  T. Sejnowski,et al.  A critique of pure vision , 1993 .

[29]  Eero P. Simoncelli,et al.  Natural image statistics and neural representation. , 2001, Annual review of neuroscience.

[30]  D. Bjorklund The role of immaturity in human development. , 1997 .

[31]  G. Lakoff,et al.  The Brain's concepts: the role of the Sensory-motor system in conceptual knowledge , 2005, Cognitive neuropsychology.

[32]  Alva Noë,et al.  Action in Perception , 2006, Representation and Mind.

[33]  R. Lickliter,et al.  Intersensory Redundancy Guides the Development of Selective Attention, Perception, and Cognition in Infancy , 2004 .

[34]  A. Clark An embodied cognitive science? , 1999, Trends in Cognitive Sciences.

[35]  D. Lewkowicz,et al.  The development of intersensory temporal perception: an epigenetic systems/limitations view. , 2000, Psychological bulletin.

[36]  Giorgio Metta,et al.  Better Vision through Manipulation , 2003, Adapt. Behav..

[37]  M. Tarr,et al.  Rotation direction affects object recognition , 2004, Vision Research.

[38]  Rolf Pfeifer,et al.  How the body shapes the way we think - a new view on intelligence , 2006 .

[39]  R A Brooks,et al.  New Approaches to Robotics , 1991, Science.

[40]  T. Poggio,et al.  Neural mechanisms of object recognition , 2002, Current Opinion in Neurobiology.

[41]  Margaret Wilson,et al.  Six views of embodied cognition , 2002, Psychonomic bulletin & review.

[42]  J. Gibson The Ecological Approach to Visual Perception , 1979 .

[43]  Florentin Wörgötter,et al.  Fast Biped Walking with a Sensor-driven Neuronal Controller and Real-time Online Learning , 2006, Int. J. Robotics Res..

[44]  R. Bajcsy Active perception , 1988 .

[45]  E Bizzi,et al.  Motor learning through the combination of primitives. , 2000, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[46]  Michael A. Arbib,et al.  The handbook of brain theory and neural networks , 1995, A Bradford book.

[47]  M. Lungarella,et al.  Information Self-Structuring: Key Principle for Learning and Development , 2005, Proceedings. The 4nd International Conference on Development and Learning, 2005..

[48]  Giulio Sandini,et al.  Developmental robotics: a survey , 2003, Connect. Sci..

[49]  Linda B. Smith,et al.  Development as a dynamic system , 1992, Trends in Cognitive Sciences.

[50]  Stefano Nolfi,et al.  Power and the limits of reactive agents , 2002, Neurocomputing.

[51]  Rolf Pfeifer,et al.  An Optimal Sensor Morphology Improves Adaptability of Neural Network Controllers , 2002, ICANN.

[52]  Marc H. Raibert,et al.  Legged Robots That Balance , 1986, IEEE Expert.

[53]  Michael Gasser,et al.  The Development of Embodied Cognition: Six Lessons from Babies , 2005, Artificial Life.

[54]  Joel L. Davis,et al.  Large-Scale Neuronal Theories of the Brain , 1994 .

[55]  Rolf Pfeifer,et al.  Sensory - motor coordination: The metaphor and beyond , 1997, Robotics Auton. Syst..

[56]  L. Steels,et al.  Grounding adaptive language games in robotic agents , 2006, AAAI 2012.

[57]  Tom Ziemke,et al.  Introduction to the special issue on situated and embodied cognition , 2002, Cognitive Systems Research.

[58]  Stevan Harnad,et al.  Cognition is categorization , 2005 .

[59]  Chandana Paul,et al.  Morphology, control and passive dynamics , 2006, Robotics Auton. Syst..

[60]  Olaf Sporns,et al.  Mapping Information Flow in Sensorimotor Networks , 2006, PLoS Comput. Biol..

[61]  Bruno A Olshausen,et al.  Sparse coding of sensory inputs , 2004, Current Opinion in Neurobiology.

[62]  Luc Berthouze,et al.  On the Interplay Between Morphological, Neural, and Environmental Dynamics: A Robotic Case Study , 2002, Adapt. Behav..

[63]  M. Goodale,et al.  Manipulating and recognizing virtual objects: where the action is. , 2001, Canadian journal of experimental psychology = Revue canadienne de psychologie experimentale.

[64]  Michael L. Anderson Embodied Cognition: A field guide , 2003, Artif. Intell..