Efficient distributed information fusion using value of information based censoring

In many distributed sensing applications, not all agents have valuable information at all times. Therefore, requiring all agents to communicate at all times can be resource intensive. In this work, the notion of Value of Information (VoI) is used to improve the efficiency of distributed sensing algorithms. Particularly, only agents with high VoI broadcast their measurements to the network, while others censor their measurements. New VoI realized data fusion algorithms are introduced, and an in depth analysis of the costs incurred by these algorithms and conventional distributed data fusion algorithms is presented. Numerical simulations are used to compare the performance of the VoI realized algorithms with traditional data fusion algorithms. A VoI based algorithm that adaptively adjusts the criterion for being informative is presented and shown to strike a good balance between reduced communication cost and increased accuracy.United States. Army Research Office (MURI grant W911NF-11-1-0391

Hi-Val: Iterative Learning of Hierarchical Value Functions for Policy Generation

Task decomposition is effective in manifold applications where the global complexity of a problem makes planning and decision-making too demanding. This is true, for example, in high-dimensional robotics domains, where (1) unpredictabilities and modeling limitations typically prevent the manual specification of robust behaviors, and (2) learning an action policy is challenging due to the curse of dimensionality. In this work, we borrow the concept of Hierarchical Task Networks (HTNs) to decompose the learning procedure, and we exploit Upper Confidence Tree (UCT) search to introduce HOP, a novel iterative algorithm for hierarchical optimistic planning with learned value functions. To obtain better generalization and generate policies, HOP simultaneously learns and uses action values. These are used to formalize constraints within the search space and to reduce the dimensionality of the problem. We evaluate our algorithm both on a fetching task using a simulated 7-DOF KUKA light weight arm and, on a pick and delivery task with a Pioneer robot

Experimental Results of Concurrent Learning Adaptive Controllers

Commonly used Proportional-Integral-Derivative based UAV flight controllers are often seen to provide adequate trajectory-tracking performance only after extensive tuning. The gains of these controllers are tuned to particular platforms, which makes transferring controllers from one UAV to other time-intensive. This paper suggests the use of adaptive controllers in speeding up the process of extracting good control performance from new UAVs. In particular, it is shown that a concurrent learning adaptive controller improves the trajectory tracking performance of a quadrotor with baseline linear controller directly imported from another quadrotors whose inertial characteristics and throttle mapping are very di fferent. Concurrent learning adaptive control uses specifi cally selected and online recorded data concurrently with instantaneous data and is capable of guaranteeing tracking error and weight error convergence without requiring persistency of excitation. Flight-test results are presented on indoor quadrotor platforms operated in MIT's RAVEN environment. These results indicate the feasibility of rapidly developing high-performance UAV controllers by using adaptive control to augment a controller transferred from another UAV with similar control assignment structure.United States. Office of Naval Research. Multidisciplinary University Research Initiative (Grant N000141110688)National Science Foundation (U.S.). Graduate Research Fellowship Program (Grant 0645960)Boeing Scientific Research Laboratorie

Bayesian Nonparametric Adaptive Control using Gaussian Processes

This technical report is a preprint of an article submitted to a journal.Most current Model Reference Adaptive Control (MRAC) methods rely on parametric adaptive elements, in which the number of parameters of the adaptive element are fixed a priori, often through expert judgment. An example of such an adaptive element are Radial Basis Function Networks (RBFNs), with RBF centers pre-allocated based on the expected operating domain. If the system operates outside of the expected operating domain, this adaptive element can become non-effective in capturing and canceling the uncertainty, thus rendering the adaptive controller only semi-global in nature. This paper investigates a Gaussian Process (GP) based Bayesian MRAC architecture (GP-MRAC), which leverages the power and flexibility of GP Bayesian nonparametric models of uncertainty. GP-MRAC does not require the centers to be preallocated, can inherently handle measurement noise, and enables MRAC to handle a broader set of uncertainties, including those that are defined as distributions over functions. We use stochastic stability arguments to show that GP-MRAC guarantees good closed loop performance with no prior domain knowledge of the uncertainty. Online implementable GP inference methods are compared in numerical simulations against RBFN-MRAC with preallocated centers and are shown to provide better tracking and improved long-term learning.This research was supported in part by ONR MURI Grant N000141110688 and NSF grant ECS #0846750

Actuator Constrained Trajectory Generation and Control for Variable-Pitch Quadrotors

Control and trajectory generation algorithms for a quadrotor helicopter with variable-pitch propellers are presented. The control law is not based on near-hover assumptions, allowing for large attitude deviations from hover. The trajectory generation algorithm ts a time-parametrized polynomial through any number of way points in R3, with a closed-form solution if the corresponding way point arrival times are known a priori. When time is not specifi ed, an algorithm for fi nding minimum-time paths subject to hardware actuator saturation limitations is presented. Attitude-specifi c constraints are easily embedded in the polynomial path formulation, allowing for aerobatic maneuvers to be performed using a single controller and trajectory generation algorithm. Experimental results on a variable pitch quadrotor demonstrate the control design and example trajectories.National Science Foundation (U.S.) (Graduate Research Fellowship under Grant No. 0645960

Off-policy reinforcement learning with Gaussian processes

An off-policy Bayesian nonparameteric approximate reinforcement learning framework, termed as GPQ, that employs a Gaussian processes (GP) model of the value (Q) function is presented in both the batch and online settings. Sufficient conditions on GP hyperparameter selection are established to guarantee convergence of off-policy GPQ in the batch setting, and theoretical and practical extensions are provided for the online case. Empirical results demonstrate GPQ has competitive learning speed in addition to its convergence guarantees and its ability to automatically choose its own bases locations.United States. Office of Naval Research (Autonomy Program N000140910625

Biosand Filter for Removal of Chemical Contaminants From Water

Numerous reports by the United Nations and the World Health Organization have indicated a significant worldwide problem with water pollution and inaccessibility to potable drinking water. Due to technological and economical barriers, the problem with water pollution is particularly more serious for under-developed and developing countries. The present study is aimed at designing, constructing and evaluating a cost-effective biosand filter was undertaken. Results indicated the removal of up to 80% total hardness, 86% chlorides, 96% turbidity and 90% colour. Moreover, the filter's performance was appraised by the absence of E. coli in the filtered sample. The filter describes the proven bioremediation technology and its ability to empower at-risk populations to use naturally occurring biology and readily available materials as a sustainable way to achieve the health benefits of safe drinking water

A critical assessment of methods for the intrinsic analysis of liquid interfaces. 1. surface site distributions

Substantial progress in our understanding of interfacial structure and dynamics has stemmed from the recent development of algorithms that allow for an intrinsic analysis of fluid interfaces. These work by identifying the instantaneous location of the interface, at the atomic level, for each molecular configuration and then computing properties relative to this location. Such a procedure eliminates the broadening of the interface caused by capillary waves and reveals the underlying features of the system. However, a precise definition of which molecules actually belong to the interfacial layer is difficult to achieve in practice. Furthermore, it is not known if the different intrinsic analysis methods are consistent with each other and yield similar results for the interfacial properties. In this paper, we carry out a systematic and detailed comparison of the available methods for intrinsic analysis of fluid interfaces, based on a molecular dynamics simulation of the interface between liquid water and carbon tetrachloride. We critically assess the advantages and shortcomings of each method, based on reliability, robustness, and speed of computation, and establish consistent criteria for determining which molecules belong to the surface layer. We believe this will significantly contribute to make intrinsic analysis methods widely and routinely applicable to interfacial systems

The Potential Energy Landscape and Mechanisms of Diffusion in Liquids

The mechanism of diffusion in supercooled liquids is investigated from the potential energy landscape point of view, with emphasis on the crossover from high- to low-T dynamics. Molecular dynamics simulations with a time dependent mapping to the associated local mininum or inherent structure (IS) are performed on unit-density Lennard-Jones (LJ). New dynamical quantities introduced include r2_{is}(t), the mean-square displacement (MSD) within a basin of attraction of an IS, R2(t), the MSD of the IS itself, and g_{loc}(t) the mean waiting time in a cooperative region. At intermediate T, r2_{is}(t) posesses an interval of linear t-dependence allowing calculation of an intrabasin diffusion constant D_{is}. Near T_{c} diffusion is intrabasin dominated with D = D_{is}. Below T_{c} the local waiting time tau_{loc} exceeds the time, tau_{pl}, needed for the system to explore the basin, indicating the action of barriers. The distinction between motion among the IS below T_{c} and saddle, or border dynamics above T_{c} is discussed.Comment: submitted to pr