Model identification and model analysis in robot training

Robot training is a fast and efficient method of obtaining robot control code. Many current machine learning paradigms used for this purpose, however, result in opaque models that are difficult, if not impossible to analyse, which is an impediment in safety-critical applications or application scenarios where humans and robots occupy the same workspace. In experiments with a Magellan Pro mobile robot we demonstrate that it is possible to obtain transparent models of sensor-motor couplings that are amenable to subsequent analysis, and how such analysis can be used to refine and tune the models post hoc

Accurate robot simulation through system identification

Robot simulators are useful tools for developing robot behaviours. They provide a fast and efficient means to test robot control code at the convenience of the office desk. In all but the simplest cases though, due to the complexities of the physical systems modelled in the simulator, there are considerable differences between the behaviour of the robot in the simulator and that in the real world environment. In this paper we present a novel method to create a robot simulator using real sensor data. Logged sensor data is used to construct a mathematically explicit model(in the form of a NARMAX polynomial) of the robot’s environment. The advantage of such a transparent model — in contrast to opaque modelling methods such as artificial neural networks — is that it can be analysed to characterise the modelled system, using established mathematical methods In this paper we compare the behaviour of the robot running a particular task in both the simulator and the real-world using qualitative and quantitative measures including statistical methods to investigate the faithfulness of the simulator

The Star Formation Rate Function of the Local Universe

We have derived the bivariate luminosity function for the far ultraviolet (1530Angstroms) and far infrared (60 microns). We used matched GALEX and IRAS data, and redshifts from NED and PSC-z. We have derived a total star formation luminosity function phi(L_{tot}), with L_{tot} = L_{FUV}+L_{FIR}. Using these, we determined the cosmic ``star formation rate'' function and density for the local universe. The total SFR function is fit very well by a log-normal distribution over five decades of luminosity. We find that the bivariate luminosity function phi(L_{FUV},L_{FIR}) shows a bimodal behavior, with L_{FIR} tracking L_{FUV} for L_{TOT}< 10^10 L_sun, and L_{FUV} saturating at 10^10 L_sun, while L_{TOT} L_{FIR} for higher luminosities. We also calculate the SFR density and compare it to other measurements.Comment: This paper will be published as part of the Galaxy Evolution Explorer (GALEX) Astrophysical Journal Letters Special Issue. Links to the full set of papers will be available at http:/www.galex.caltech.edu/PUBLICATIONS/ after November 22, 200

Understanding the role of the Biological Pump in the Global Carbon Cycle: An imperative for Ocean Science.

Anthropogenically driven climate change will rapidly become Earth's dominant transformative influence in the coming decades. The oceanic biological pump—the complex suite of processes that results in the transfer of particulate and dissolved organic carbon from the surface to the deep ocean—constitutes the main mechanism for removing CO2 from the atmosphere and sequestering carbon at depth on submillennium time scales. Variations in the efficacy of the biological pump and the strength of the deep ocean carbon sink, which is larger than all other bioactive carbon reservoirs, regulate Earth's climate and have been implicated in past glacial-​interglacial cycles. The numerous biological, chemical, and physical processes involved in the biological pump are inextricably linked and heterogeneous over a wide range of spatial and temporal scales, and they influence virtually the entire ocean ecosystem. Thus, the functioning of the oceanic biological pump is not only relevant to the modulation of Earth's climate but also constitutes the basis for marine biodiversity and key food resources that support the human population. Our understanding of the biological pump is far from complete. Moreover, how the biological pump and the deep ocean carbon sink will respond to the rapid and ongoing anthropogenic changes to our planet—including warming, acidification, and deoxygenation of ocean waters—remains highly uncertain. To understand and quantify present-day and future changes in biological pump processes requires sustained global observations coupled with extensive modeling studies supported by international scientific coordination and fundin