Evolved Electrophysiological Soft Robots

The embodied cognition paradigm emphasizes that both bodies and brains combine to produce complex behaviors, in contrast to the traditional view that the only seat of intelligence is the brain. Despite recent excitement about embodied cognition, brains and bodies remain thought of, and implemented as, two separate entities that merely interface with one another to carry out their respective roles. Previous research co-evolving bodies and brains has simulated the physics of bodies that collect sensory information and pass that information on to disembodied neural networks, which then processes that information and return motor commands. Biological animals, in contrast, produce behavior through physically embedded control structures and a complex and continuous interplay between neural and mechanical forces. In addition to the electrical pulses flowing through the physical wiring of the nervous system, the heart elegantly combines control with actuation, as the physical properties of the tissue itself (or defects therein) determine the actuation of the organ. Inspired by these phenomena from cardiac electrophysiology (the study of the electrical properties of heart tissue), we introduce electrophysiological robots, whose behavior is dictated by electrical signals flowing though the tissue cells of soft robots. Here we describe these robots and how they are evolved. Videos and images of these robots reveal lifelike behaviors despite the added challenge of having physically embedded control structures. We also provide an initial experimental investigation into the impact of different implementation decisions, such as alternatives for sensing, actuation, and locations of central pattern generators. Overall, this paper provides a first step towards removing the chasm between bodies and brains to encourage further research into physically realistic embodied cognition. Introduction and Background The fields of evolutionary robotics and artificial life have seen a great deal of emphasis on embodied cognition in recent years [Cheney et al. (2013); Bongard (2013); Rieffel et al. (2013); Auerbach and Bongard (2012); Hiller and Lipson (2012a); Lehman and Stanley (2011); Auerbach and Bongard (2010a,b); Pfeifer et al. (2007); Hornby et al. (2001); Lipson and Pollack (2000)]. There is even a paradigm called embodied cognition, which argues that the specifics of the embodiment (such as the morphology) are Figure 1: Current flowing through an evolved creature. The legend for voltage within each cell (colors) is given in Fig. 3. vital parts of the resulting behavior of the system: It argues that the co-evolutionary connection between body and brain is more deeply intertwined than the body simply acting as a minimal physical interface between the brain and the environment [Pfeifer and Bongard (2006)]. Recent work in evolutionary robotics has shown that complex behaviors can arise when co-evolving bodies and brains. At one end of the spectrum, Auerbach and Bongard (2010b) demonstrated the evolution of physical structures that had no joints or actuators, and evolved to cover the largest distance in a controlled fall due to gravity. While that work exemplifies the evolution of behavior emerging from morphology alone, it does not co-evolve any actuation or control. Auerbach and Bongard (2010a) then evolved the placement of CPG controlled rotational joints between cellular spheres, thus co-evolving morphology and control. Cheney et al. (2013) evolved locomoting soft robots made of multiple different materials: two passive voxels of differing rigidity and two actuated voxel types that expanded cyclically via out-of-phase central pattern generators (CPGs). While this work added a variety of soft materials and a new type of actuation, the pairing of muscle types directly to a CPG again reflected a focus on evolving morphology rather than sophisticated neural control. Many examples in the literature include the co-evolution of a robot morphology with an artificial neural network controller [Sims (1994); Lipson and Pollack (2000); Hornby et al. (2001); Lehman and Stanley (2011)]. These studies (and many more like them) involve what might be called “ghost” networks: artificial neural networks that provide control to the body, yet do not have any physical embodiALIFE 14: Proceedings of the Fourteenth International Conference on the Synthesis and Simulation of Living Systems Figure 2: An example of complex electrical wave propagation in cardiac modeling [Fenton et al. (2005)]. ment in the system they control. The state of input nodes to these networks is often set by sensors in the robot and output nodes typically signify behavioral outcomes in the actuators, but the computation is done supernaturally, disjoint from the body itself. In the age of 3D printing, it is a realistic goal for robots to physically walk out of a printer. It is thus worthwhile to consider designing robots that can be physically realized: i.e., those whose controllers are accounted for by being physically woven into the design of the robot. While the brains of animals are often a separate module within their bodies, animals also have central and peripheral nervous systems extending throughout their bodies. An extreme example of this is the octopus, which has as much as 90% of its neurons existing outside of its central nervous system [Zullo et al. (2009)]. The distributed and physical layout of the nervous system over space may contribute significantly to neural processing, as the delays and branching in axons (the basis for nerves) are suggested to serve computational functions [Segev and Schneidman (1999)]. Despite the prevalence of embodied, distributed circuitry in nearly all of animal life, the idea of an embodied nervous system has been absent from the field of evolutionary robotics. The sub-field called Evolvable Hardware evolves physical circuits for computer chips [Floreano and Mattiussi (2008)], but such work has not been applied to evolving the circuitry of artificial life organisms. We are unaware of work with virtual creatures that have physically embodied control systems (e.g. where neural circuitry physically runs throughout the body of the creature). We present the first such work in this paper. We propose a very basic model of electrical signal propagation throughout the body of an evolved creature. This embodied controller is based on electrophysiology (specifically at large scales, such as cardiac electrophysiology, Fig. 2). Electrophysiology is the study of the electrical properties of biological cells and tissues [Hoffman et al. (1960)]. In this model, electrical pulses from a single centralized sinusoidal pacemaker (analogous to the sinoatrial node – the pacemaker in the heart [Brown (1982)]) are propagated through the electrically conductive tissue of the creature. The location and patterning of this conductive tissue is described by an evolved Compositional Pattern Producing Network (CPPN) genome. Evolution controls the shape of the body and the electrical pathways within it, which both combine to determine the robot’s behavior. The model involves conductive tissue cells that collect voltage from neighboring cells, causing an action potential (spike) if the collected voltage exceeds the cell’s firing threshold (Fig. 3). Once this threshold is crossed, the cell depolarizes, causing a voltage spike that excites neighboring cells. This voltage spike is followed by a refractory period, during which the cell is temporarily unable to be re-excited. This model allows for the propagation of information through the body of the creature in the form of electrical signals. The structure of this flow is produced entirely by the topology of the creature and the state of each cell’s direct neighbors. In this sense, the model can be seen as a form of distributed information processing. One could draw similarities between this model and a 3D-grid of neurons, where each neuron receives inputs from, and has outputs to, its immediate neighbors. In this analogy, we are evolving where neurons should exist in the grid, what type of material the neuron is housed in, as well as the material type, if any, of grid locations that do not contain neurons. The placement of material, which is under evolutionary control, directly determines the resultant behavior of the organism. Cells that actuate will contract and expand as they depolarize (much like the contraction of cardiac muscles), leading to the locomotion behavior of the creature. In order to control the signal flow throughout the creature, insulator cells are allowed, which are unable to accept and pass on the signal. Evolution can also choose not to fill a voxel with material. The morphology of the simulated robot and tissue type at each cell is determined by a CPPN genome. This model examines the evolution of embodied cognition at a more detailed level of implementation than is typical in the literature – with embodied control circuitry resulting directly from the morphology of the individual creature. While this study only covers the classic problem of locomotion, it is a step towards truly physically embodied robots.