Morphological Evolution of Physical Robots through Model-Free Phenotype Development.

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.This study was supported by the Swiss National Science Foundation Grant No. PP00P2123387/1 and the ETH Zurich Research Grant ETH-23-10-3. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.This is the final version of the article. It first appeared from [publisher] via http://dx.doi.org/1371/journal.pone.012844

Evolutionary Developmental Robotics: Improving Morphology and Control of Physical Robots.

Evolutionary algorithms have previously been applied to the design of morphology and control of robots. The design space for such tasks can be very complex, which can prevent evolution from efficiently discovering fit solutions. In this article we introduce an evolutionary-developmental (evo-devo) experiment with real-world robots. It allows robots to grow their leg size to simulate ontogenetic morphological changes, and this is the first time that such an experiment has been performed in the physical world. To test diverse robot morphologies, robot legs of variable shapes were generated during the evolutionary process and autonomously built using additive fabrication. We present two cases with evo-devo experiments and one with evolution, and we hypothesize that the addition of a developmental stage can be used within robotics to improve performance. Moreover, our results show that a nonlinear system-environment interaction exists, which explains the nontrivial locomotion patterns observed. In the future, robots will be present in our daily lives, and this work introduces for the first time physical robots that evolve and grow while interacting with the environment.This research was supported by the RoboSoft - Coordination Action for Soft Robotics, funded by the European Commission under the Future and Emerging Technologies - (FP7-ICT-2013-C project no 619319)

An extendible reconfigurable robot based on hot melt adhesives

The ability to physically enlarge one's own body structures plays an important role in robustness and adaptability of biological systems. It is, however, a significant challenge for robotic systems to autonomously extend their bodies. To address this challenge, this paper presents an approach using hot melt adhesives (HMAs) to assemble and integrate extensions into the robotic body. HMAs are thermoplastics with temperature dependent adhesiveness and bonding strength. We exploit this property of HMAs to connect passive external objects to the robot's own body structures, and investigate the characteristics of the approach. In a set of elementary configurations, we analyze to which extent a robot can self-reconfigure using the proposed method. We found that the extension limit depends on the mechanical properties of the extension, and the reconfiguration algorithm. A five-axis robot manipulator equipped with specialized HMA handling devices is employed to demonstrate these findings in four experiments. It is shown that the robot can construct and integrate extensions into its own body, which allow it to solve tasks that it could not achieve in its initial configuration

An extendible reconfigurable robot based on hot melt adhesives

The ability to physically enlarge one’s own body structures plays an important role in robustness and adaptability of biological systems. It is, however, a significant challenge for robotic systems to autonomously extend their bodies. To address this challenge, this paper presents an approach using hot melt adhesives (HMAs) to assemble and integrate extensions into the robotic body. HMAs are thermoplastics with temperature dependent adhesiveness and bonding strength. We exploit this property of HMAs to connect passive external objects to the robot’s own body structures, and investigate the characteristics of the approach. In a set of elementary configurations, we analyze to which extent a robot can self-reconfigure using the proposed method. We found that the extension limit depends on the mechanical properties of the extension, and the reconfiguration algorithm. A five-axis robot manipulator equipped with specialized HMA handling devices is employed to demonstrate these findings in four experiments. It is shown that the robot can construct and integrate extensions into its own body, which allow it to solve tasks that it could not achieve in its initial configuration.ISSN:0929-5593ISSN:1573-752

Results of the real-world and theoretical experiments.

<p>^aMean fitness of the best three individuals in the first/last generation</p><p>Results of the real-world and theoretical experiments.</p

Construction process.

<p>This example illustrates the execution of the third (and last) gene of the genome shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0128444#pone.0128444.t001" target="_blank">Table 1</a>. (a) The initial configuration shows the state after construction of the first two genes, i.e. a passive element is fixed onto an active module (labeled as structure) and a second active element is prepared. (b-c) Rotation of the structure by -90° around the z-axis and 90° around y. (d) Rotation of the new element by -90° around y. (e-f) HMA is applied to the structure and the module is connected on top of the structure according to the position offset parameters Δx and Δy. (g) An end-rotation of the whole agent by 90° around the y-axis is performed. (h) The motor control parameters are assigned to the active elements and the agent can be evaluated.</p

Evolution for a locomotion task.

<p>In each of the five experiments, ten generations with ten robots were built and tested. For each robot of experiment 2, an image of its top-view at the beginning of the evaluation process is shown. The number on the top-left corner of each image indicates its fitness (cm/s). The lines between generations show the relations between robots, i.e. the method for generating the new genotype (solid black: elite; thin black: crossover; thin grey: mutation). Negative fitnesses and missing images indicate failure of the building process of the respective robot. The images show that various types of robots are tested, and the fitness of the robots increases in the course of the experiment.</p

Exploration of the design space.

<p>Each agent can be located in the design space given its number of elements and shape factor (A,B,C). The grey area shows the theoretical limit which can be reached using the cubic modules. The portion of this space that can be covered is restricted by the constraints which apply to the real-world construction process. The colored areas illustrate the parts of the design space that can be reached with different sets of active constraints. All constraints are active in (A), the stability condition is relaxed in (B) and no constraints are active in (C). This subsequently increases the reachable portion of the design space. The solid black markers indicate the distribution of the agents in experiment 1b (A) and experiment 2 (B). Their area is proportional to the number of agents in the bin. The right column (D-I) shows two example morphologies per experiment.</p