An Experimental Evaluation of Bayesian Optimization on Bipedal Locomotion

© 2014 IEEE.The design of gaits and corresponding control policies for bipedal walkers is a key challenge in robot locomotion. Even when a viable controller parametrization already exists, finding near-optimal parameters can be daunting. The use of automatic gait optimization methods greatly reduces the need for human expertise and time-consuming design processes. Many different approaches to automatic gait optimization have been suggested to date. However, no extensive comparison among them has yet been performed. In this paper, we present some common methods for automatic gait optimization in bipedal locomotion, and analyze their strengths and weaknesses. We experimentally evaluated these gait optimization methods on a bipedal robot, in more than 1800 experimental evaluations. In particular, we analyzed Bayesian optimization in different configurations, including various acquisition functions

Bayesian Gait Optimization for Bipedal Locomotion

One of the key challenges in robotic bipedal locomotion is finding gait parameters that optimize a desired performance criterion, such as speed, robustness or energy efficiency. Typically, gait optimization requires extensive robot experiments and specific expert knowledge. We propose to apply data-driven machine learning to automate and speed up the process of gait optimization. In particular, we use Bayesian optimization to efficiently find gait parameters that optimize the desired performance metric. As a proof of concept we demonstrate that Bayesian optimization is near-optimal in a classical stochastic optimal control framework. Moreover, we validate our approach to Bayesian gait optimization on a low-cost and fragile real bipedal walker and show that good walking gaits can be efficiently found by Bayesian optimization. © 2014 Springer International Publishing

Superconductivity without Fe or Ni in the phosphides BaIr2P2 and BaRh2P2

Heat capacity, resistivity, and magnetic susceptibility measurements confirm bulk superconductivity in single crystals of BaIr$_2$P$_2$ (T$_c$=2.1K) and BaRh$_2$P$_2$ (T$_c$ = 1.0 K). These compounds form in the ThCr$_2$Si$_2$ (122) structure so they are isostructural to both the Ni and Fe pnictides but not isoelectronic to either of them. This illustrates the importance of structure for the occurrence of superconductivity in the 122 pnictides. Additionally, a comparison between these and other ternary phosphide superconductors suggests that the lack of interlayer $P-P$ bonding favors superconductivity. These stoichiometric and ambient pressure superconductors offer an ideal playground to investigate the role of structure for the mechanism of superconductivity in the absence of magnetism.Comment: Published in Phys Rev B: Rapid Communication

Bayesian Optimization for Learning Gaits under Uncertainty

© 2015, Springer International Publishing Switzerland.Designing gaits and corresponding control policies is a key challenge in robot locomotion. Even with a viable controller parametrization, finding near-optimal parameters can be daunting. Typically, this kind of parameter optimization requires specific expert knowledge and extensive robot experiments. Automatic black-box gait optimization methods greatly reduce the need for human expertise and time-consuming design processes. Many different approaches for automatic gait optimization have been suggested to date. However, no extensive comparison among them has yet been performed. In this article, we thoroughly discuss multiple automatic optimization methods in the context of gait optimization. We extensively evaluate Bayesian optimization, a model-based approach to black-box optimization under uncertainty, on both simulated problems and real robots. This evaluation demonstrates that Bayesian optimization is particularly suited for robotic applications, where it is crucial to find a good set of gait parameters in a small number of experiments

Fermi surface instabilities in CeRh2Si2 at high magnetic field and pressure

We present thermoelectric power (TEP) studies under pressure and high magnetic field in the antiferromagnet CeRh2Si2 at low temperature. Under magnetic field, large quantum oscillations are observed in the TEP, S(H), in the antiferromagnetic phase. They suddenly disappear when entering in the polarized paramagnetic (PPM) state at Hc pointing out an important reconstruction of the Fermi surface (FS). Under pressure, S/T increases strongly of at low temperature near the critical pressure Pc, where the AF order is suppressed, implying the interplay of a FS change and low energy excitations driven by spin and valence fluctuations. The difference between the TEP signal in the PPM state above Hc and in the paramagnetic state (PM) above Pc can be explained by different FS. Band structure calculations at P = 0 stress that in the AF phase the 4f contribution at the Fermi level (EF) is weak while it is the main contribution in the PM domain. By analogy to previous work on CeRu2Si2, in the PPM phase of CeRh2Si2 the 4f contribution at EF will drop.Comment: 10 pages, 13 figure

Perceptually Motivated Wavelet Packet Transform for Bioacoustic Signal Enhancement

A significant and often unavoidable problem in bioacoustic signal processing is the presence of background noise due to an adverse recording environment. This paper proposes a new bioacoustic signal enhancement technique which can be used on a wide range of species. The technique is based on a perceptually scaled wavelet packet decomposition using a species-specific Greenwood scale function. Spectral estimation techniques, similar to those used for human speech enhancement, are used for estimation of clean signal wavelet coefficients under an additive noise model. The new approach is compared to several other techniques, including basic bandpass filtering as well as classical speech enhancement methods such as spectral subtraction, Wiener filtering, and Ephraim–Malah filtering. Vocalizations recorded from several species are used for evaluation, including the ortolan bunting (Emberiza hortulana), rhesus monkey (Macaca mulatta), and humpback whale (Megaptera novaeanglia), with both additive white Gaussian noise and environment recording noise added across a range of signal-to-noise ratios (SNRs). Results, measured by both SNR and segmental SNR of the enhanced wave forms, indicate that the proposed method outperforms other approaches for a wide range of noise conditions

Superconducting phase diagram of the filled skuterrudite PrOs4Sb12

We present new measurements of the specific heat of the heavy fermion superconductor PrOs4Sb12, on a sample which exhibits two sharp distinct anomalies at Tc1= 1.89K and Tc2= 1.72K. They are used to draw a precise magnetic field-temperature superconducting phase diagram of PrOs4Sb12 down to 350 mK. We discuss the superconducting phase diagram of PrOs4Sb12 and its possible relation with an unconventional superconducting order parameter. We give a detailed analysis of Hc2(T), which shows paramagnetic limitation (a support for even parity pairing) and multiband effects

Multiband superconductivity in the heavy fermion compound PrOs4Sb12

The thermal conductivity of the heavy fermion superconductor PrOs4Sb12 was measured down to Tc/40 throughout the vortex state. At lowest temperatures and for magnetic fields H ~ 0.07Hc2, already 40% of the normal state thermal conductivity is restored. This behaviour (similar to that observed in MgB2) is a clear signature of multiband superconductivity in this compound.Comment: 12pages, version #1 20\_06\_200

Direct observation of the quantum critical point in heavy fermion CeRhSi3_3

We report on muon spin rotation studies of the noncentrosymmetric heavy fermion antiferromagnet CeRhSi$_3$. A drastic and monotonic suppression of the internal fields, at the lowest measured temperature, was observed upon an increase of external pressure. Our data suggest that the ordered moments are gradually quenched with increasing pressure, in a manner different from the pressure dependence of the N\'eel temperature. At \unit{23.6}{kbar}, the ordered magnetic moments are fully suppressed via a second-order phase transition, and $T_{\rm{N}}$ is zero. Thus, we directly observed the quantum critical point at \unit{23.6}{kbar} hidden inside the superconducting phase of CeRhSi$_3$

Fermi-surface topology of the iron pnictide LaFe2_2P2_2

We report on a comprehensive de Haas--van Alphen (dHvA) study of the iron pnictide LaFe$_2$P$_2$. Our extensive density-functional band-structure calculations can well explain the measured angular-dependent dHvA frequencies. As salient feature, we observe only one quasi-two-dimensional Fermi-surface sheet, i.e., a hole-like Fermi-surface cylinder around $\Gamma$, essential for $s_\pm$ pairing, is missing. In spite of considerable mass enhancements due to many-body effects, LaFe$_2$P$_2$ shows no superconductivity. This is likely caused by the absence of any nesting between electron and hole bands.Comment: 5 pages, 4 figure