Electrophoretic separation of proteins via complexation with a polyelectrolyte

We suggest to augment standard isoelectronic focusing for separation of proteins in a gradient of pH by a similar focusing in the presence of a strongly charged polyelectrolyte (PE). Proteins which have the same isoelectric point but different "hidden" charge of both signs in pI point make complexes with PE, which focus in different pH. This is a result of charge inversion of such proteins by adsorbed PE molecules, which is sensitive to the hidden charge. Hence the hidden charge is a new separation parameter

Learning a Structured Neural Network Policy for a Hopping Task

In this work we present a method for learning a reactive policy for a simple dynamic locomotion task involving hard impact and switching contacts where we assume the contact location and contact timing to be unknown. To learn such a policy, we use optimal control to optimize a local controller for a fixed environment and contacts. We learn the contact-rich dynamics for our underactuated systems along these trajectories in a sample efficient manner. We use the optimized policies to learn the reactive policy in form of a neural network. Using a new neural network architecture, we are able to preserve more information from the local policy and make its output interpretable in the sense that its output in terms of desired trajectories, feedforward commands and gains can be interpreted. Extensive simulations demonstrate the robustness of the approach to changing environments, outperforming a model-free gradient policy based methods on the same tasks in simulation. Finally, we show that the learned policy can be robustly transferred on a real robot.Comment: IEEE Robotics and Automation Letters 201

Pattern Generation for Walking on Slippery Terrains

In this paper, we extend state of the art Model Predictive Control (MPC) approaches to generate safe bipedal walking on slippery surfaces. In this setting, we formulate walking as a trade off between realizing a desired walking velocity and preserving robust foot-ground contact. Exploiting this formulation inside MPC, we show that safe walking on various flat terrains can be achieved by compromising three main attributes, i. e. walking velocity tracking, the Zero Moment Point (ZMP) modulation, and the Required Coefficient of Friction (RCoF) regulation. Simulation results show that increasing the walking velocity increases the possibility of slippage, while reducing the slippage possibility conflicts with reducing the tip-over possibility of the contact and vice versa.Comment: 6 pages, 7 figure

Unsupervised Contact Learning for Humanoid Estimation and Control

This work presents a method for contact state estimation using fuzzy clustering to learn contact probability for full, six-dimensional humanoid contacts. The data required for training is solely from proprioceptive sensors - endeffector contact wrench sensors and inertial measurement units (IMUs) - and the method is completely unsupervised. The resulting cluster means are used to efficiently compute the probability of contact in each of the six endeffector degrees of freedom (DoFs) independently. This clustering-based contact probability estimator is validated in a kinematics-based base state estimator in a simulation environment with realistic added sensor noise for locomotion over rough, low-friction terrain on which the robot is subject to foot slip and rotation. The proposed base state estimator which utilizes these six DoF contact probability estimates is shown to perform considerably better than that which determines kinematic contact constraints purely based on measured normal force.Comment: Submitted to the IEEE International Conference on Robotics and Automation (ICRA) 201

Object Distribution Networks for World-wide Document Circulation

This paper presents an Object Distribution System (ODS), a distributed system inspired by the ultra-large scale distribution models used in everyday life (e.g. food or newspapers distribution chains). Beyond traditional mechanisms of approaching information to readers (e.g. caching and mirroring), this system enables the publication, classification and subscription to volumes of objects (e.g. documents, events). Authors submit their contents to publication agents. Classification authorities provide classification schemes to classify objects. Readers subscribe to topics or authors, and retrieve contents from their local delivery agent (like a kiosk or library, with local copies of objects). Object distribution is an independent process where objects circulate asynchronously among distribution agents. ODS is designed to perform specially well in an increasingly populated, widespread and complex Internet jungle, using weak consistency replication by object distribution, asynchronous replication, and local access to objects by clients. ODS is based on two independent virtual networks, one dedicated to the distribution (replication) of objects and the other to calculate optimised distribution chains to be applied by the first network

On Time Optimization of Centroidal Momentum Dynamics

Recently, the centroidal momentum dynamics has received substantial attention to plan dynamically consistent motions for robots with arms and legs in multi-contact scenarios. However, it is also non convex which renders any optimization approach difficult and timing is usually kept fixed in most trajectory optimization techniques to not introduce additional non convexities to the problem. But this can limit the versatility of the algorithms. In our previous work, we proposed a convex relaxation of the problem that allowed to efficiently compute momentum trajectories and contact forces. However, our approach could not minimize a desired angular momentum objective which seriously limited its applicability. Noticing that the non-convexity introduced by the time variables is of similar nature as the centroidal dynamics one, we propose two convex relaxations to the problem based on trust regions and soft constraints. The resulting approaches can compute time-optimized dynamically consistent trajectories sufficiently fast to make the approach realtime capable. The performance of the algorithm is demonstrated in several multi-contact scenarios for a humanoid robot. In particular, we show that the proposed convex relaxation of the original problem finds solutions that are consistent with the original non-convex problem and illustrate how timing optimization allows to find motion plans that would be difficult to plan with fixed timing.Comment: 7 pages, 4 figures, ICRA 201