Unscented Bayesian Optimization for Safe Robot Grasping

We address the robot grasp optimization problem of unknown objects considering uncertainty in the input space. Grasping unknown objects can be achieved by using a trial and error exploration strategy. Bayesian optimization is a sample efficient optimization algorithm that is especially suitable for this setups as it actively reduces the number of trials for learning about the function to optimize. In fact, this active object exploration is the same strategy that infants do to learn optimal grasps. One problem that arises while learning grasping policies is that some configurations of grasp parameters may be very sensitive to error in the relative pose between the object and robot end-effector. We call these configurations unsafe because small errors during grasp execution may turn good grasps into bad grasps. Therefore, to reduce the risk of grasp failure, grasps should be planned in safe areas. We propose a new algorithm, Unscented Bayesian optimization that is able to perform sample efficient optimization while taking into consideration input noise to find safe optima. The contribution of Unscented Bayesian optimization is twofold as if provides a new decision process that drives exploration to safe regions and a new selection procedure that chooses the optimal in terms of its safety without extra analysis or computational cost. Both contributions are rooted on the strong theory behind the unscented transformation, a popular nonlinear approximation method. We show its advantages with respect to the classical Bayesian optimization both in synthetic problems and in realistic robot grasp simulations. The results highlights that our method achieves optimal and robust grasping policies after few trials while the selected grasps remain in safe regions.Comment: conference pape

Computation of Equilibria in OLGModels with Many Heterogeneous Households

This paper develops a decomposition algorithm by which a market economy with many households may be solved through the computation of equilibria for a sequence of representative agent economies. The paper examines local and global convergence properties of the sequential recalibration (SR) algorithm. SR is then demonstrated to efficiently solve Auerbach- Kotlikoff OLG models with a large number of heterogeneous households. We approximate equilibria in OLG models by solving a sequence of related Ramsey optimal growth problems. This approach can provide improvements in both efficiency and robustness as compared with simultaneous solution methods.Computable general equilibrium, Overlapping generations, Microsimulation, Sequential recalibration

Closed-loop Bayesian Semantic Data Fusion for Collaborative Human-Autonomy Target Search

In search applications, autonomous unmanned vehicles must be able to efficiently reacquire and localize mobile targets that can remain out of view for long periods of time in large spaces. As such, all available information sources must be actively leveraged -- including imprecise but readily available semantic observations provided by humans. To achieve this, this work develops and validates a novel collaborative human-machine sensing solution for dynamic target search. Our approach uses continuous partially observable Markov decision process (CPOMDP) planning to generate vehicle trajectories that optimally exploit imperfect detection data from onboard sensors, as well as semantic natural language observations that can be specifically requested from human sensors. The key innovation is a scalable hierarchical Gaussian mixture model formulation for efficiently solving CPOMDPs with semantic observations in continuous dynamic state spaces. The approach is demonstrated and validated with a real human-robot team engaged in dynamic indoor target search and capture scenarios on a custom testbed.Comment: Final version accepted and submitted to 2018 FUSION Conference (Cambridge, UK, July 2018

Efficient Iterative Processing in the SciDB Parallel Array Engine

Many scientific data-intensive applications perform iterative computations on array data. There exist multiple engines specialized for array processing. These engines efficiently support various types of operations, but none includes native support for iterative processing. In this paper, we develop a model for iterative array computations and a series of optimizations. We evaluate the benefits of an optimized, native support for iterative array processing on the SciDB engine and real workloads from the astronomy domain

Massively Parallel Computation Using Graphics Processors with Application to Optimal Experimentation in Dynamic Control

The rapid increase in the performance of graphics hardware, coupled with recent improvements in its programmability has lead to its adoption in many non-graphics applications, including wide variety of scientific computing fields. At the same time, a number of important dynamic optimal policy problems in economics are athirst of computing power to help overcome dual curses of complexity and dimensionality. We investigate if computational economics may benefit from new tools on a case study of imperfect information dynamic programming problem with learning and experimentation trade-off that is, a choice between controlling the policy target and learning system parameters. Specifically, we use a model of active learning and control of linear autoregression with unknown slope that appeared in a variety of macroeconomic policy and other contexts. The endogeneity of posterior beliefs makes the problem difficult in that the value function need not be convex and policy function need not be continuous. This complication makes the problem a suitable target for massively-parallel computation using graphics processors. Our findings are cautiously optimistic in that new tools let us easily achieve a factor of 15 performance gain relative to an implementation targeting single-core processors and thus establish a better reference point on the computational speed vs. coding complexity trade-off frontier. While further gains and wider applicability may lie behind steep learning barrier, we argue that the future of many computations belong to parallel algorithms anyway.Graphics Processing Units, CUDA programming, Dynamic programming, Learning, Experimentation

Labor market policy evaluation with an agent-based model

I develop an agent-based computational economics (ACE) model with which I evaluate the aggregate impact of labor market policies. The findings are that governmentfinanced training measures increase the outflow rate from unemployment to employment. Although the overall effect is positive this effect is achieved by reducing the outflow rate for those who do not receive subsidies. Furthermore, the outflow rate would have been downward-biased had one supposed a matching function that is exogenous to policies. -- Im Folgenden wird ein agenten-basiertes Modell entwickelt, mit dem die aggregierten Wirkungen von Arbeitsmarktpolitiken evaluiert werde können. Ein Resultat ist, dass die Subvention von Trainingsmaßnahmen die Übergangsrate von Arbeitslosigkeit in Beschäftigung erhöht. Obwohl der Gesamteffekt positiv ist, reduziert sich die Übergangsrate für all jene Arbeitslose, deren Ausgaben nicht subventioniert werden. Der Verdrängungseffekt ist bei einer plausiblen Parametrisierung des Modells in seiner Höhe ökonomisch relevant. Ferner wäre die Messung der Übergangsrate aus Arbeitslosigkeit in Beschäftigung nach unten verzerrt gewesen, hätte man in der Wirkungsanalyse angenommen, dass die Matching-Funktion exogen zu den Arbeitsmarktpolitiken ist.

Introducing CGE Models to the Classroom Using EXCEL

This paper demonstrates how simple general equilibrium models can be solved with the help of Microsoft Excel. Two different general equilibrium models for tax incidence analysis are used as illustrative examples. The methods presented here are intended to be beneficial to both students and teachers working with general equilibrium theory in the classroom and can easily be extended to various policy analysis term projects. The techniques presented here are simple and effective tools for inclusion in any student’s toolkit.Excel, Solver, General Equilibrium, Optimization, Newton’s Method