Locomotion Without a Brain: Physical Reservoir Computing in Tensegrity Structures
- 1 January 2013
- journal article
- research article
- Published by MIT Press in Artificial Life
- Vol. 19 (1), 35-66
- https://doi.org/10.1162/artl_a_00080
Abstract
Embodiment has led to a revolution in robotics by not thinking of the robot body and its controller as two separate units, but taking into account the interaction of the body with its environment. By investigating the effect of the body on the overall control computation, it has been suggested that the body is effectively performing computations, leading to the term morphological computation. Recent work has linked this to the field of reservoir computing, allowing one to endow morphologies with a theory of universal computation. In this work, we study a family of highly dynamic body structures, called tensegrity structures, controlled by one of the simplest kinds of “brains.” These structures can be used to model biomechanical systems at different scales. By analyzing this extreme instantiation of compliant structures, we demonstrate the existence of a spectrum of choices of how to implement control in the body-brain composite. We show that tensegrity structures can maintain complex gaits with linear feedback control and that external feedback can intrinsically be integrated in the control loop. The various linear learning rules we consider differ in biological plausibility, and no specific assumptions are made on how to implement the feedback in a physical system.This publication has 60 references indexed in Scilit:
- Information Processing Capacity of Dynamical SystemsScientific Reports, 2012
- Information processing using a single dynamical node as complex systemNature Communications, 2011
- High-speed microscopic imaging of flagella motility and swimming in Giardia lamblia trophozoitesProceedings of the National Academy of Sciences of the United States of America, 2011
- A Reward-Modulated Hebbian Learning Rule Can Explain Experimentally Observed Network Reorganization in a Brain Control TaskJournal of Neuroscience, 2010
- Morphological communication: exploiting coupled dynamics in a complex mechanical structure to achieve locomotionJournal of The Royal Society Interface, 2009
- Generating Coherent Patterns of Activity from Chaotic Neural NetworksNeuron, 2009
- Robustness of Learning That Is Based on Covariance-Driven Synaptic PlasticityPLoS Computational Biology, 2008
- Computational Aspects of Feedback in Neural CircuitsPLoS Computational Biology, 2007
- Operant matching is a generic outcome of synaptic plasticity based on the covariance between reward and neural activityProceedings of the National Academy of Sciences of the United States of America, 2006
- Simplified neuron model as a principal component analyzerJournal of Mathematical Biology, 1982