Morphological Evolution of Physical Robots through Model-Free Phenotype Development

Abstract

Artificial evolution of physical systems is a stochastic optimization method in which physical machines are iteratively adapted to a target function. The key for a meaningful design optimization is the capability to build variations of physical machines through the course of the evolutionary process. The optimization in turn no longer relies on complex physics models that are prone to the reality gap, a mismatch between simulated and real-world behavior. We report model-free development and evaluation of phenotypes in the artificial evolution of physical systems, in which a mother robot autonomously designs and assembles locomotion agents. The locomotion agents are automatically placed in the testing environment and their locomotion behavior is analyzed in the real world. This feedback is used for the design of the next iteration. Through experiments with a total of 500 autonomously built locomotion agents, this article shows diversification of morphology and behavior of physical robots for the improvement of functionality with limited resources.

The trade-off between morphology and control in the co-optimized design of robots

“…In our previous works we opted for a background subtraction and a feature extraction to identify robots [22], but this method required a high computation power in parallel to the homeostasis of our system (Trajectory of the end-effector, BO calculations, etc.). In here we opted for a chessboard pattern extraction, commonly available as a library for OpenCV, in which the system simultaneously identifies the corners of a 4x5 chessboard in four cameras.…”

“…In a real-world experiment with evolutionary robots [22] a significant improvement in the final MC design could be seen, but the process required the construction of 500 robots to reach the best design. EAs are capable of optimizing systems, but the lack of an assumption over the influence of its parameters and the need to build every candidate makes it a poor method for MC optimization of robots.…”

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

“…The only alternative is to optimize in hardware directly, which is in general slow and costly. Brodbeck et al [7] provide an interesting illustration how locomoting cube-like creatures can be evolved in a model-free fashion through automated manufacturing and testing. However, in summary, the design decisionswhich parameters to optimize-are based on heuristics and a clear methodology is still missing.…”

Toward Energy Autonomy in Heterogeneous Modular Plant-Inspired Robots through Artificial Evolution

“…Improvements to the evolutionary algorithm itself would be valuable to cope with this. These improvements could consist of implementing speciation (Stanley and Miikkulainen, 2002), novelty search (Lehman and Stanley, 2011), and/or pareto optimization techniques (Schmidt and Lipson, 2011;Brodbeck et al, 2015). However, doing more evolutionary runs might lead to the discovery of better performing individuals by having an initial population that can be exposed to incremental improvements.…”