Acquiring moving skills in robots with evolvable morphologies: Recent results and outlook

© 2017 ACM. We construct and investigate a strongly embodied evolutionary system, where not only the controllers but also the morphologies undergo evolution in an on-line fashion. In these studies, we have been using various types of robot morphologies and controller architectures in combination with several learning algorithms, e.g. evolutionary algorithms, reinforcement learning, simulated annealing, and HyperNEAT. This hands-on experience provides insights and helps us elaborate on interesting research directions for future development

Predicting polarization enhancement in multicomponent ferroelectric superlattices

Ab initio calculations are utilized as an input to develop a simple model of polarization in epitaxial short-period CaTiO3/SrTiO3/BaTiO3 superlattices grown on a SrTiO3 substrate. The model is then combined with a genetic algorithm technique to optimize the arrangement of individual CaTiO3, SrTiO3 and BaTiO3 layers in a superlattice, predicting structures with the highest possible polarization and a low in-plane lattice constant mismatch with the substrate. This modelling procedure can be applied to a wide range of layered perovskite-oxide nanostructures providing guidance for experimental development of nanoelectromechanical devices with substantially improved polar properties.Comment: 4 pages, submitted to PR

Finding a Highly Connected Steiner Subgraph and its Applications

Given a (connected) undirected graph G, a set X ? V(G) and integers k and p, the Steiner Subgraph Extension problem asks whether there exists a set S ? X of at most k vertices such that G[S] is a p-edge-connected subgraph. This problem is a natural generalization of the well-studied Steiner Tree problem (set p = 1 and X to be the terminals). In this paper, we initiate the study of Steiner Subgraph Extension from the perspective of parameterized complexity and give a fixed-parameter algorithm (i.e., FPT algorithm) parameterized by k and p on graphs of bounded degeneracy (removing the assumption of bounded degeneracy results in W-hardness). Besides being an independent advance on the parameterized complexity of network design problems, our result has natural applications. In particular, we use our result to obtain new single-exponential FPT algorithms for several vertex-deletion problems studied in the literature, where the goal is to delete a smallest set of vertices such that: (i) the resulting graph belongs to a specified hereditary graph class, and (ii) the deleted set of vertices induces a p-edge-connected subgraph of the input graph

Lossy Kernels for Hitting Subgraphs

In this paper, we study the Connected H-hitting Set and Dominating Set problems from the perspective of approximate kernelization, a framework recently introduced by Lokshtanov et al. [STOC 2017]. For the Connected H-hitting set problem, we obtain an alpha-approximate kernel for every alpha>1 and complement it with a lower bound for the natural weighted version. We then perform a refined analysis of the tradeoff between the approximation factor and kernel size for the Dominating Set problem on d-degenerate graphs and provide an interpolation of approximate kernels between the known d^2-approximate kernel of constant size and 1-approximate kernel of size k^{O(d^2)}

Analysing the relative importance of robot brains and bodies

The evolution of robots, when applied to both the morphologies and the controllers, is not only a means to obtain high-quality robot designs, but also a process that results in many body-brain-fitness data points. Inspired by this perspective, in this paper we investigate the relative importance of robot bodies and brains for a good fitness. We introduce a method to isolate and quantify the effect of the bodies and brains on the quality of the robots and perform a case study. The method is general in that it is not restricted to evolutionary systems. For the case study, we use a system of modular robots, where the bodies are evolvable and the brains are evolvable and learnable. These case studies validate the usefulness of our method and deliver interesting insights into the interplay between bodies and brains in evolutionary robotics

Differential Evolution with Reversible Linear Transformations

Differential evolution (DE) is a well-known type of evolutionary algorithms (EA). Similarly to other EA variants it can suffer from small populations and loose diversity too quickly. This paper presents a new approach to mitigate this issue: We propose to generate new candidate solutions by utilizing reversible linear transformations applied to a triplet of solutions from the population. In other words, the population is enlarged by using newly generated individuals without evaluating their fitness. We assess our methods on three problems: (i) benchmark function optimization, (ii) discovering parameter values of the gene repressilator system, (iii) learning neural networks. The empirical results indicate that the proposed approach outperforms vanilla DE and a version of DE with applying differential mutation three times on all testbeds

Scapular Acceleration during Upper Extremity Elevation in Healthy Individuals with and without Scapular Dyskinesis

Background/Purpose: Individuals with upper extremity pathology often present with altered scapular motion and muscle performance. There are few clinical tools that are capable of collecting specific and efficient data on alterations in scapular motion and even fewer studies have looked at variations in scapular acceleration. The primary purpose of this study was to determine the effectiveness of wireless accelerometers for detecting changes in acceleration in individuals with and without scapular dyskinesis. Methods: Twenty-seven subjects, mean age 24 (SD1.49). Healthy subjects were visually screened for scapular dyskinesis. Subjects were positioned in a standardized standing posture and anatomical references were marked on the scapula for the wireless accelerometer (MyoResearch 3D DTS). After the accelerometer was secured, subjects performed five repetitions of standing scaption from 0-140. Linear scapular accelerations along three orthogonal axes (x, y, and z) were collected during arm elevation and lowering. For the first 9 subjects, the entire process was repeated 1-2 days later. Data was synthesized in order to reflect changes in acceleration from the resting position. Intraclass correlation coefficients (ICC 3, k) were used to determine the between-day intra-rater reliability. An independent t-test was used to determine the difference in average axis acceleration between those with and without dyskinesis. A one-way multivariate analysis of variance (MANOVA) was used to determine differences in acceleration between those with and without dyskinesis for each accelerometer axis while adjustments were made for multiple comparisons. Results: There was good intra-rater reliability for the x and y axes (ICC\u3e.80). There was a significant increase in overall acceleration of the scapula in those with dyskinesis (P=.039). There was also a significant increase in acceleration across the x-axis for those with dyskinesis (P=.003). Conclusion: Wireless accelerometers are a reliable tool for quantifying scapular motion in healthy individuals with and without dyskinesis. In a healthy population with dyskinesis, the overall magnitude of scapular acceleration was greater when compared to a healthy group without dyskinesis. Clinical Relevance: Dyskinetic subjects present with increased scapular acceleration in elevation implicating potential muscle imbalances that need to be further investigated in future research.https://ecommons.udayton.edu/dpt_symposium/1002/thumbnail.jp