Tutorial on Embodiment

5.3.1. Body schema and forward models*

Concepts that are currently being studied, mainly in neuroscience and psychology, are ‘body schema' (e.g., De Preester & Knockaert, 2005; Haggard & Wolpert, 2005; Maravita et al., 2003) and ‘forward', or internal, models (Bays & Wolpert, 2007; Webb, 2004; Wolpert et al., 1998). The body schema can be viewed as the sensory-motor ‘representation' of the agent's body and its action possibilities. Forward models enable agents to predict the consequences of their actions and are related to anticipatory behavior (e.g., Pezzulo, 2007).

In more concrete terms, for instance, in the (uncertain, dynamic, potentially hostile) world out there, it may be of advantage to:

  1. Predict the next sensory feedback in advance - for instance, during rapid locomotion, biological feedback is too slow.
  2. Distinguish self-generated sensory information from sensory input genera­ted by the environment, leading to detection of changes in the environment. For instance, it feels different when we move our eyes than when the world moves, although on the retina it may look the same.
  3. Simulate different courses of action and choose the one with the best consequences. Whereas it is not surprising that humans possess such capabili­ties, they have been discovered even in much simpler animals. For instance, prediction is demonstrated in the motor preparation of the prey-catching behavior of the jumping spider (Schomaker, 2004). As another example, rats are able to compare alternative paths in a T-maze before actually acting, thus ‘planning in simulation' (Hesslow, 2002).

Both concepts - body schema and forward models - have also direct relevance for robotics (see e.g., Hoffmann et al., 2010, for a review). Robots that can autonomously develop and adapt models of their bodies (which are usually necessary for their control) can greatly reduce costs associated with their programming. Moreover, the could be able to adapt to new circumstances, such as use of a tool or failure. We want to refer the interested reader to two talks on this topic:

Online Lecture:

Micha Hersch
University of Lausanne
Learning a peripersonal space representation for a humanoid robot

In this talk, Micha Hersch describes the experiments he has conducted with humanoid robots. Through self-observation, the robots build a model of their body and the space within their reach. This is adapted if the robot starts using a tool. Related reference: Hersch et al. (2008).


Online Lecture:

Josh Bongard
University of Vermont
Burlington, USA
Resilient Machines

In this talk, Josh Bongard describes his resilient machines experiments. A quadruped robot synthesizes a model of its body in a physics-based simulator. Later, it can use this model to develop new behaviors. Moreover, on failure - a leg breaks off - it can detect that, update the model, and develop behaviors that compensate for this failure. Related reference: Bongard et al. (2006).


*This section has been adapted from Hoffmann & Pfeifer, 2011.


Bays, P. M. & Wolpert, D. M. (2007), 'Computational principles of sensorimotor control that minimize uncertainty and variability', Journal of Physiology 578(Pt 2), 387--396.
Bongard, J.; Zykov, V. & Lipson, H. (2006), 'Resilient machines through continuous self-modeling', Science 314, 1118-1121.
De Preester, H. & Knockaert, K. (2005), Body Image and Body Schema - interdisciplinary perspectives on the body, John Benjamins.
Haggard, P. & Wolpert, D. M.Freund, H.; Jeannerod, M.; M., H. & Leiguarda, R., ed., (2005), Higher-order motor disorders, Oxford University Press, chapter Disorders of body scheme.
Hersch, M.; Sauser, E. & Billard, A. (2008), 'Online learning of the body schema', International Journal of Humanoid Robotics 5, 161-181.
Hesslow, G. (2002), 'Conscious thought as simulation of behaviour and perception', Trends in Cognitive Sciences 6, 242-247.
Hoffmann, M.; Marques, H.; Hernandez Arieta, A.; Sumioka, H.; Lungarella, M. & Pfeifer, R. (2010), 'Body schema in robotics: a review', IEEE Trans. Auton. Mental Develop. 2 (4), 304-324.
Hoffmann, M. & Pfeifer, R. (2011), The implications of embodiment for behavior and cognition: animal and robotic case studies, in W. Tschacher & C. Bergomi, ed., 'The Implications of Embodiment: Cognition and Communication', Exeter: Imprint Academic, pp. 31-58.
Maravita, A.; Spence, C. & Driver, J. (2003), 'Multisensory integration and the body schema: close to hand and within reach.', Curr Biol 13(13), R531--R539.
Pezzulo, G. (2007). Anticipation and Future-Oriented Capabilities in Natural and Artificial Cognition. In M. Lungarella, F. Iida, J. C. Bongard, & R. Pfeifer, (eds.), 50 Years of AI, Festschrift (pp. 258-71). Berlin: Springer.
Schomaker, L. (2004), Anticipation in cybernetic systems: a case against mindless anti-representationalism., in 'Proc. Int. Conf. Systems, Man and Cybernetics', pp. 2037 - 2045.
Webb, B. (2004), 'Neural mechanisms for prediction: do insects have forward models?', Trends in Neurosciences 27, 278-282.
Wolpert, D. M.; Miall, R. C. & Kawato, M. (1998), 'Internal models in the cerebellum', Trends in Cognitive Sciences 2(9), 338-347.