Optimal control and inverse optimal control by distribution matching

Optimal control is a powerful approach to achieve optimal behavior. However, it typically requires a manual specification of a cost function which often contains several objectives, such as reaching goal positions at different time steps or energy efficiency. Manually trading-off these objectives is often difficult and requires a high engineering effort. In this paper, we present a new approach to specify optimal behavior. We directly specify the desired behavior by a distribution over future states or features of the states. For example, the experimenter could choose to reach certain mean positions with given accuracy/variance at specified time steps. Our approach also unifies optimal control and inverse optimal control in one framework. Given a desired state distribution, we estimate a cost function such that the optimal controller matches the desired distribution. If the desired distribution is estimated from expert demonstrations, our approach performs inverse optimal control. We evaluate our approach on several optimal and inverse optimal control tasks on non-linear systems using incremental linearizations similar to differential dynamic programming approaches

Learning to predict phases of manipulation tasks as hidden states

Phase transitions in manipulation tasks often occur when contacts between objects are made or broken. A switch of the phase can result in the robot’s actions suddenly influencing different aspects of its environment. Therefore, the boundaries between phases often correspond to constraints or subgoals of the manipulation task. In this paper, we investigate how the phases of manipulation tasks can be learned from data. The task is modeled as an autoregressive hidden Markov model, wherein the hidden phase transitions depend on the observed states. The model is learned from data using the expectation-maximization algorithm. We demonstrate the proposed method on both a pushing task and a pepper mill turning task. The proposed approach was compared to a standard autoregressive hidden Markov model. The experiments show that the learned models can accurately predict the transitions in phases during the manipulation tasks

Formal concept analysis and structures underlying quantum logics

A Hilbert space $H$ induces a formal context, the Hilbert formal context $\overline H$, whose associated concept lattice is isomorphic to the lattice of closed subspaces of $H$. This set of closed subspaces, denoted $\mathcal C(H)$, is important in the development of quantum logic and, as an algebraic structure, corresponds to a so-called ``propositional system'', that is, a complete, atomistic, orthomodular lattice which satisfies the covering law. In this paper, we continue with our study of the Chu construction by introducing the Chu correspondences between Hilbert contexts, and showing that the category of Propositional Systems, PropSys, is equivalent to the category of $\text{ChuCors}_{\mathcal H}$ of Chu correspondences between Hilbert contextsUniversidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Emergence of the pointer basis through the dynamics of correlations

We use the classical correlation between a quantum system being measured and its measurement apparatus to analyze the amount of information being retrieved in a quantum measurement process. Accounting for decoherence of the apparatus, we show that these correlations may have a sudden transition from a decay regime to a constant level. This transition characterizes a non-asymptotic emergence of the pointer basis, while the system-apparatus can still be quantum correlated. We provide a formalization of the concept of emergence of a pointer basis in an apparatus subject to decoherence. This contrast of the pointer basis emergence to the quantum to classical transition is demonstrated in an experiment with polarization entangled photon pairs.Comment: 4+2 pgs, 3 figures. Title changed. Revised version to appear on PR

In silico proteomic analysis provides insights into phylogenomics and plant biomass deconstruction potentials of the Tremelalles

Basidiomycetes populate a wide range of ecological niches but unlike ascomycetes, their capabilities to decay plant polymers and their potential for biotechnological approaches receive less attention. Particularly, identification and isolation of CAZymes is of biotechnological relevance and has the potential to improve the cache of currently available commercial enzyme cocktails toward enhanced plant biomass utilization. The order Tremellales comprises phylogenetically diverse fungi living as human pathogens, mycoparasites, saprophytes or associated with insects. Here, we have employed comparative genomics approaches to highlight the phylogenomic relationships among thirty-five Tremellales and to identify putative enzymes of biotechnological interest encoded on their genomes. Evaluation of the predicted proteomes of the thirty-five Tremellales revealed 6,918 putative carbohydrate-active enzymes (CAZYmes) and 7,066 peptidases. Two soil isolates, Saitozyma podzolica DSM 27192 and Cryptococcus sp. JCM 24511, show higher numbers harboring an average of 317 compared to a range of 267–121 CAZYmes for the rest of the strains. Similarly, the proteomes of the two soil isolates along with two plant associated strains contain higher number of peptidases sharing an average of 234 peptidases compared to a range of 226–167 for the rest of the strains. Despite these huge differences and the apparent enrichment of these enzymes among the soil isolates, the data revealed a diversity of the various enzyme families that does not reflect specific habitat type. Growth experiment on various carbohydrates to validate the predictions provides support for this view. Overall, the data indicates that the Tremellales could serve as a rich source of both CAZYmes and peptidases with wide range of potential biotechnological relevance

Interaction primitives for human-robot cooperation tasks

To engage in cooperative activities with human partners, robots have to possess basic interactive abilities and skills. However, programming such interactive skills is a challenging task, as each interaction partner can have different timing or an alternative way of executing movements. In this paper, we propose to learn interaction skills by observing how two humans engage in a similar task. To this end, we introduce a new representation called Interaction Primitives. Interaction primitives build on the framework of dynamic motor primitives (DMPs) by maintaining a distribution over the parameters of the DMP. With this distribution, we can learn the inherent correlations of cooperative activities which allow us to infer the behavior of the partner and to participate in the cooperation. We will provide algorithms for synchronizing and adapting the behavior of humans and robots during joint physical activities

Mechanical similarity as a generalization of scale symmetry

In this paper we study the symmetry known as mechanical similarity (LMS) and present for any monomial potential. We analyze it in the framework of the Koopman-von Neumann formulation of classical mechanics and prove that in this framework the LMS can be given a canonical implementation. We also show that the LMS is a generalization of the scale symmetry which is present only for the inverse square potential. Finally we study the main obstructions which one encounters in implementing the LMS at the quantum mechanical level.Comment: 9 pages, Latex, a new section adde

A feature-based comparison of local search and the Christofides algorithm for the travelling salesperson problem

Understanding the behaviour of well-known algorithms for classical NP-hard optimisation problems is still a difficult task. With this paper, we contribute to this research direction and carry out a feature based comparison of local search and the well-known Christofides approximation algorithm for the Traveling Salesperson Problem. We use an evolutionary algorithm approach to construct easy and hard instances for the Christofides algorithm, where we measure hardness in terms of approximation ratio. Our results point out important features and lead to hard and easy instances for this famous algorithm. Furthermore, our cross-comparison gives new insights on the complementary benefits of the different approaches.Samadhi Nallaperuma, Markus Wagner, Frank Neumann, Bernd Bischl, Olaf Mersmann, Heike Trautmannhttp://www.sigevo.org/foga-2013

Global surface slopes and roughness of the Moon from the Lunar Orbiter Laser Altimeter

The acquisition of new global elevation data from the Lunar Orbiter Laser Altimeter, carried on the Lunar Reconnaissance Orbiter, permits quantification of the surface roughness properties of the Moon at unprecedented scales and resolution. We map lunar surface roughness using a range of parameters: median absolute slope, both directional (along-track) and bidirectional (in two dimensions); median differential slope; and Hurst exponent, over baselines ranging from ~17 m to ~2.7 km. We find that the lunar highlands and the mare plains show vastly different roughness properties, with subtler variations within mare and highlands. Most of the surface exhibits fractal-like behavior, with a single or two different Hurst exponents over the given baseline range; when a transition exists, it typically occurs near the 1 km baseline, indicating a significant characteristic spatial scale for competing surface processes. The Hurst exponent is high within the lunar highlands, with a median value of 0.95, and lower in the maria (with a median value of 0.76). The median differential slope is a powerful tool for discriminating between roughness units and is useful in characterizing, among other things, the ejecta surrounding large basins, particularly Orientale, as well as the ray systems surrounding young, Copernican-age craters. In addition, it allows a quantitative exploration on mare surfaces of the evolution of surface roughness with age