Animals sustain the ability to operate after injury by creating qualitatively different compensatory behaviors. Although such robustness would be desirable in engineered systems, most machines fail in the face of unexpected damage. We describe a robot that can recover from such change autonomously, through continuous self-modeling. A four-legged machine uses actuation-sensation relationships to indirectly infer its own structure, and it then uses this self-model to generate forward locomotion. When a leg part is removed, it adapts the self-models, leading to the generation of alternative gaits. This concept may help develop more robust machines and shed light on self-modeling in animals.

Self-reproduction is central to biological life for long-term sustainability and evolutionary adaptation. Although these traits would also be desirable in many engineered systems, the principles of self-reproduction have not been exploited in machine design. Here we create simple machines that act as autonomous modular robots and are capable of physical self-reproduction using a set of cubes.

Abstract-Here we introduce one simulated and two physical three-dimensional stochastic modular robot systems, all capable of self-assembly and self-reconfiguration. We assume that individual units can only draw power when attached to the growing structure, and have no means of actuation. Instead they are subject to random motion induced by the surrounding medium when unattached. We present a simulation environment with a flexible scripting language that allows for parallel and serial selfassembly and self-reconfiguration processes. We explore factors that govern the rate of assembly and reconfiguration, and show that self-reconfiguration can be exploited to accelerate the assembly of a particular shape, as compared with static self-assembly. We then demonstrate the ability of two different physical three-dimensional stochastic modular robot systems to self-reconfigure in a fluid. The second physical implementation is only composed of technologies that could be scaled down to achieve stochastic self-assembly and self-reconfiguration at the microscale.

An impressive variety of systems have been designed with capabilities such as forming, growing, reconfiguring, repairing and replicating themselves, based on information coded in their components.

Co-evolution of system models and system tests can be used for exploratory system identification of physical platforms. Here we demonstrate how the amount of physical testing can be reduced by managing the difficulty that a population of tests poses to a population of candidate models. If test difficulty is not managed, then disengagement between the two populations occurs: The difficulty of the evolved test data supplied to the model population may grow faster than the ability of the models to explain them. Here we use variance of model outputs for a given test as a predictor of the tests' difficulty. Proper engagement of the co-evolving populations is achieved by evolving tests that induce a particular amount of variance. We demonstrate this claim by identifying nonlinear dynamical systems using nonlinear models and linear approximation models.