Towards a theoretical foundation for morphological computation with compliant bodies

Abstract

The control of compliant robots is, due to their often nonlinear and complex dynamics, inherently difficult. The vision of morphological computation proposes to view these aspects not only as problems, but rather also as parts of the solution. Non-rigid body parts are not seen anymore as imperfect realizations of rigid body parts, but rather as potential computational resources. The applicability of this vision has already been demonstrated for a variety of complex robot control problems. Nevertheless, a theoretical basis for understanding the capabilities and limitations of morphological computation has been missing so far. We present a model for morphological computation with compliant bodies, where a precise mathematical characterization of the potential computational contribution of a complex physical body is feasible. The theory suggests that complexity and nonlinearity, typically unwanted properties of robots, are desired features in order to provide computational power. We demonstrate that simple generic models of physical bodies, based on mass-spring systems, can be used to implement complex nonlinear operators. By adding a simple readout (which is static and linear) to the morphology such devices are able to emulate complex mappings of input to output streams in continuous time. Hence, by outsourcing parts of the computation to the physical body, the difficult problem of learning to control a complex body, could be reduced to a simple and perspicuous learning task, which can not get stuck in local minima of an error function. Abstract The control of compliant robots is, due to their often nonlinear and complex dynamics, inherently difficult. The vision of morphological computation proposes to view these aspects not only as problems, but rather also as parts of the solution. Non-rigid body parts are not seen anymore as imperfect realizations of rigid body parts, but rather as potential computational resources. The applicability of this vision has already been demonstrated for a variety of complex robot control problems. Nevertheless, a theoretical basis for understanding the capabilities and limitations of morphological computation has been missing so far. We present a model for morphological computation with compliant bodies, where a precise mathematical characterization of the potential computational contribution of a complex physical body is feasible. The theory suggests that complexity and nonlinearity, typically unwanted properties of robots, are desired features in order to provide computational power. We demonstrate that simple generic models of physical bodies, based on mass-spring systems, can be used to implement complex nonlinear operators. By adding a simple readout (which is static and linear) to the morphology, such devices are able to emulate complex mappings of input to output streams in continuous time. Hence, by outsourcing parts of the computation to the physical body, the difficult problem of learning to control a complex body, could be reduced to a simple and perspicuous le...

Cellular automata modelling of slime mould actin network signalling

“…As such, the complexity of the organism is reduced whilst enabling the spontaneous generation of complex behaviours-in effect, this is a definition of emergence. These observations are complimentary to recent advances in the field of morphological computation and entity embodiment which posit that 'outsourcing' a certain amount of computational work to the morphology of data streams is essential in the design of artificially intelligent entities (Hauser et al 2012;Lungarella and Sporns 2006). This concept is intimately linked to data 'structuring'-transducing and transmitting signals in a repeatable and unambiguous manner-which is another important concept in morphological computation and control theory (Füchslin Hauser et al 2012).…”

“…These observations are complimentary to recent advances in the field of morphological computation and entity embodiment which posit that 'outsourcing' a certain amount of computational work to the morphology of data streams is essential in the design of artificially intelligent entities (Hauser et al 2012;Lungarella and Sporns 2006). This concept is intimately linked to data 'structuring'-transducing and transmitting signals in a repeatable and unambiguous manner-which is another important concept in morphological computation and control theory (Füchslin Hauser et al 2012). By logical extension, this would seem to suggest that the cytoskeleton is a medium for structuring sensorimotor data streams and hence that the emergent behaviours displayed by cells may be a product of cytoskeletal processes.…”

Simple or Complex Bodies? Trade-offs in Exploiting Body Morphology for Control

“…The spirit of the morphological computation literature that follows the "offloading" or "trade-off" perspective, is that complex (highly dimensional, dynamic, nonlinear, compliant, deformable, "soft" ) bodies are advantageous for control because they can take over the "computation" that a controller would otherwise have to perform (e.g., [15,16,39] or [9] explicitly in Fig. 1).…”

“…robots-thus "offloading" computational processing from a central con-troller to the morphology. These uses of morphology for explanation and engineering are sometimes referred to as "morphological computation" (e.g., [15,16,39]). However, in our view, only some of the characteristic cases that are embraced by the community as instances of morphological computation have a truly computational flavor.…”

A multi-level control architecture for the bionic handling assistant

“…Prominent examples include artificial salamanders [1], hexapods [2], snakes [3], worms [4], and smaller quadrupeds [5]. These platforms showcase the interplay of morphology and computation [6] and explore the benefit of highly flexible continuum robots for future applications, like minimal invasive surgery [7]. They also provide a way to implement the understanding-bybuilding paradigm towards analysis of biological systems, e.g.…”