Simulating Robots Without Conventional Physics: A Neural Network Approach
The construction of physics-based simulators for use in Evolutionary Robotics (ER) can be complex and time-consuming. Alternative simulation schemes construct robotic simulators from empirically-collected data. Such empirical simulators, however, also have associated challenges. This paper therefore investigates the potential use of Artificial Neural Networks, henceforth simply referred to as Neural Networks (NNs), as alternative robotic simulators. In contrast to physics models, NN-based simulators can be constructed without requiring an explicit mathematical model of the system being modeled, which can simplify simulator development. The generalization abilities of NNs, along with NNs' noise tolerance, suggest that NNs could be well-suited to application in robotics simulation. Investigating whether NNs can be effectively used as robotic simulators in ER is thus the endeavour of this work. Two robot morphologies were selected on which the NN simulators created in this work were based, namely a differentially steered robot and an inverted pendulum robot. Accuracy tests indicated that NN simulators created for these robots generally trained well and could generalize well on data not presented during simulator construction. In order to validate the feasibility of the created NN simulators in the ER process, these simulators were subsequently used to evolve controllers in simulation, similar to controllers developed in related studies. Encouraging results were obtained, with the newly-evolved controllers allowing experimental robots to exhibit obstacle avoidance, light-approaching behaviour and inverted pendulum stabilization. It was thus clearly established that NN-based robotic simulators can be successfully employed as alternative simulation schemes in the ER process.
evolutionary robotics robotic simulation artificial neural networks controller development