Deep Reinforcement Learning for Tensegrity Robot Locomotion

Tensegrity robots, composed of rigid rods connected by elastic cables, have a number of unique properties that make them appealing for use as planetary exploration rovers. However, control of tensegrity robots remains a difficult problem due to their unusual structures and complex dynamics. In this work, we show how locomotion gaits can be learned automatically using a novel extension of mirror descent guided policy search (MDGPS) applied to periodic locomotion movements, and we demonstrate the effectiveness of our approach on tensegrity robot locomotion. We evaluate our method with real-world and simulated experiments on the SUPERball tensegrity robot, showing that the learned policies generalize to changes in system parameters, unreliable sensor measurements, and variation in environmental conditions, including varied terrains and a range of different gravities. Our experiments demonstrate that our method not only learns fast, power-efficient feedback policies for rolling gaits, but that these policies can succeed with only the limited onboard sensing provided by SUPERball's accelerometers. We compare the learned feedback policies to learned open-loop policies and hand-engineered controllers, and demonstrate that the learned policy enables the first continuous, reliable locomotion gait for the real SUPERball robot. Our code and other supplementary materials are available from http://rll.berkeley.edu/drl_tensegrityComment: International Conference on Robotics and Automation (ICRA), 2017. Project website link is http://rll.berkeley.edu/drl_tensegrit

Optimized parameter search for large datasets of the regularization parameter and feature selection for ridge regression

In this paper we propose mathematical optimizations to select the optimal regularization parameter for ridge regression using cross-validation. The resulting algorithm is suited for large datasets and the computational cost does not depend on the size of the training set. We extend this algorithm to forward or backward feature selection in which the optimal regularization parameter is selected for each possible feature set. These feature selection algorithms yield solutions with a sparse weight matrix using a quadratic cost on the norm of the weights. A naive approach to optimizing the ridge regression parameter has a computational complexity of the order with the number of applied regularization parameters, the number of folds in the validation set, the number of input features and the number of data samples in the training set. Our implementation has a computational complexity of the order . This computational cost is smaller than that of regression without regularization for large datasets and is independent of the number of applied regularization parameters and the size of the training set. Combined with a feature selection algorithm the algorithm is of complexity and for forward and backward feature selection respectively, with the number of selected features and the number of removed features. This is an order faster than and for the naive implementation, with for large datasets. To show the performance and reduction in computational cost, we apply this technique to train recurrent neural networks using the reservoir computing approach, windowed ridge regression, least-squares support vector machines (LS-SVMs) in primal space using the fixed-size LS-SVM approximation and extreme learning machines

A nation of vicars and merchants’: religiosity and Dutch MEPs

Religion is a major driving force in the Dutch political system and as European integration has progressed, it is often argued that these national practices affect how national representatives act in the European Parliament (EP). Our aim in this study is to determine to what extent the religious divide impacts upon the work of Dutch MEPs in the European political arena. On the basis of the RelEP survey and interviews, we argue that religious or secular views are very salient to Dutch MEPs, but that their impact is largely indirect. Moreover, we find that Dutch MEPs actively use the EP and its committee system in an attempt to redefine the relationship between church and state in the Netherlands. And finally, we argue that the European arena offers new opportunities for mobilisation among those promoting secularist interests

Shorten new product development and introduction in bio(pharmaceutical) industries

Introduction Nowadays, the production of monoclonal antibody using animal cell culture (MAB) requires new improvements. The approach presented in this poster is based on simulation, scheduling optimization and de-bottlenecking of production processes. Mainly an abstraction of the P&I D (Process and instrumentation diagram) is done and the control software for a Biotech Plan is modeled. The modeling of the control software is based on ISA standards: ISA-88 and ISA-95. These standards are crucial for optimization and debottlenecking of processes. The simulation of a small scale production facilitates to recognize lowlights and highlights in the production , improving the results when it is applied to a full scale production

Oral delivery of glutamic acid decarboxylase (GAD)-65 and IL10 by lactococcus lactis reverses diabetes in recent-onset NOD mice

Growing insight into the pathogenesis of type 1 diabetes (T1D) and numerous studies in preclinical models highlight the potential of antigen-specific approaches to restore tolerance efficiently and safely. Oral administration of protein antigens is a preferred method for tolerance induction, but degradation during gastrointestinal passage can impede such protein-based therapies, reducing their efficacy and making them cost-ineffective. To overcome these limitations, we generated a tolerogenic bacterial delivery technology based on live Lactococcus lactis (LL) bacteria for controlled secretion of the T1D autoantigen GAD65370-575 and the anti-inflammatory cytokine interleukin-10 in the gut. In combination with short-course low-dose anti-CD3, this treatment stabilized insulitis, preserved functional β-cell mass, and restored normoglycemia in recent-onset NOD mice, even when hyperglycemia was severe at diagnosis. Combination therapy did not eliminate pathogenic effector T cells, but increased the presence of functional CD4+Foxp3 +CD25+ regulatory T cells. These preclinical data indicate a great therapeutic potential of orally administered autoantigen-secreting LL for tolerance induction in T1D. © 2014 by the American Diabetes Association.SCOPUS: ar.jinfo:eu-repo/semantics/publishe