Episodic Learning with Control Lyapunov Functions for Uncertain Robotic Systems

Many modern nonlinear control methods aim to endow systems with guaranteed properties, such as stability or safety, and have been successfully applied to the domain of robotics. However, model uncertainty remains a persistent challenge, weakening theoretical guarantees and causing implementation failures on physical systems. This paper develops a machine learning framework centered around Control Lyapunov Functions (CLFs) to adapt to parametric uncertainty and unmodeled dynamics in general robotic systems. Our proposed method proceeds by iteratively updating estimates of Lyapunov function derivatives and improving controllers, ultimately yielding a stabilizing quadratic program model-based controller. We validate our approach on a planar Segway simulation, demonstrating substantial performance improvements by iteratively refining on a base model-free controller

A data driven approach to landslide susceptibility mapping in Great Britain

Landslides are a geo-hazard which can have significant societal impacts including loss of human life, physical damage to infrastructure and financial loss. The ability to assess where landslides will occur is therefore of great interest for the public good and can be approached both theoretically and empirically. With the ever increasing availability of spatial data, information on landslide events is now much more readily available ranging from initiation point coordinates to high (sub-metre) resolution topographic information and associated derivatives on affected (and unaffected) areas. Coupled with information on the geology of a region, it is possible to build up a detailed location specific profile of past events, all of which may prove useful for informing where future events may occur. We present preliminary results from an assessment of various data to reassess current British landslide susceptibility datasets. These could be used in future to provide additional information to support landslide forecasting. We define susceptibility as: The potential for the occurrence of a hazard within a specified area. This is currently provided for by the BGS GeoSure Landslides product [1] which classifies landslide prone areas on an A-E (low-high) basis, based on heuristics as well as consideration of lithology, discontinuities and slope angle. Data-driven analyses may provide further insights into where and why landslides occur. Using this knowledge, we hope to improve our current landslide susceptibility model. Consequently, this will enable us to be more confident in the identification of areas where landslides may occur in the future

A Control Lyapunov Perspective on Episodic Learning via Projection to State Stability

The goal of this paper is to understand the impact of learning on control synthesis from a Lyapunov function perspective. In particular, rather than consider uncertainties in the full system dynamics, we employ Control Lyapunov Functions (CLFs) as low-dimensional projections. To understand and characterize the uncertainty that these projected dynamics introduce in the system, we introduce a new notion: Projection to State Stability (PSS). PSS can be viewed as a variant of Input to State Stability defined on projected dynamics, and enables characterizing robustness of a CLF with respect to the data used to learn system uncertainties. We use PSS to bound uncertainty in affine control, and demonstrate that a practical episodic learning approach can use PSS to characterize uncertainty in the CLF for robust control synthesis

Operator method in solving non-linear equations of the Hartree-Fock type

The operator method is used to construct the solutions of the problem of the polaron in the strong coupling limit and of the helium atom on the basis of the Hartree-Fock equation. $E_0=-0.1085128052\alpha^2$ is obtained for the polaron ground-state energy. Energies for 2s- and 3s-states are also calculated. The other excited states are briefly discussed.Comment: 7 page

Episodic Learning with Control Lyapunov Functions for Uncertain Robotic Systems

Many modern nonlinear control methods aim to endow systems with guaranteed properties, such as stability or safety, and have been successfully applied to the domain of robotics. However, model uncertainty remains a persistent challenge, weakening theoretical guarantees and causing implementation failures on physical systems. This paper develops a machine learning framework centered around Control Lyapunov Functions (CLFs) to adapt to parametric uncertainty and unmodeled dynamics in general robotic systems. Our proposed method proceeds by iteratively updating estimates of Lyapunov function derivatives and improving controllers, ultimately yielding a stabilizing quadratic program model-based controller. We validate our approach on a planar Segway simulation, demonstrating substantial performance improvements by iteratively refining on a base model-free controller

A Control Lyapunov Perspective on Episodic Learning via Projection to State Stability

The goal of this paper is to understand the impact of learning on control synthesis from a Lyapunov function perspective. In particular, rather than consider uncertainties in the full system dynamics, we employ Control Lyapunov Functions (CLFs) as low-dimensional projections. To understand and characterize the uncertainty that these projected dynamics introduce in the system, we introduce a new notion: Projection to State Stability (PSS). PSS can be viewed as a variant of Input to State Stability defined on projected dynamics, and enables characterizing robustness of a CLF with respect to the data used to learn system uncertainties. We use PSS to bound uncertainty in affine control, and demonstrate that a practical episodic learning approach can use PSS to characterize uncertainty in the CLF for robust control synthesis

Spin-Nematic Squeezed Vacuum in a Quantum Gas

Using squeezed states it is possible to surpass the standard quantum limit of measurement uncertainty by reducing the measurement uncertainty of one property at the expense of another complementary property. Squeezed states were first demonstrated in optical fields and later with ensembles of pseudo spin-1/2 atoms using non-linear atom-light interactions. Recently, collisional interactions in ultracold atomic gases have been used to generate a large degree of quadrature spin squeezing in two-component Bose condensates. For pseudo spin-1/2 systems, the complementary properties are the different components of the total spin vector , which fully characterize the state on an SU(2) Bloch sphere. Here, we measure squeezing in a spin-1 Bose condensate, an SU(3) system, which requires measurement of the rank-2 nematic or quadrupole tensor as well to fully characterize the state. Following a quench through a nematic to ferromagnetic quantum phase transition, squeezing is observed in the variance of the quadratures up to -8.3(-0.7 +0.6) dB (-10.3(-0.9 +0.7) dB corrected for detection noise) below the standard quantum limit. This spin-nematic squeezing is observed for negligible occupation of the squeezed modes and is analogous to optical two-mode vacuum squeezing. This work has potential applications to continuous variable quantum information and quantum-enhanced magnetometry

Coherent states for the hydrogen atom: discrete and continuous spectra

We construct the systems of generalised coherent states for the discrete and continuous spectra of the hydrogen atom. These systems are expressed in elementary functions and are invariant under the $SO(3, 2)$ (discrete spectrum) and $SO(4, 1)$ (continuous spectrum) subgroups of the dynamical symmetry group $SO(4, 2)$ of the hydrogen atom. Both systems of coherent states are particular cases of the kernel of integral operator which interwines irreducible representations of the $SO(4, 2)$ group.Comment: 15 pages, LATEX, minor sign corrections, to appear in J.Phys.

The Physics of Hadronic Tau Decays

Hadronic tau decays represent a clean laboratory for the precise study of quantum chromodynamics (QCD). Observables (sum rules) based on the spectral functions of hadronic tau decays can be related to QCD quark-level calculations to determine fundamental quantities like the strong coupling constant, parameters of the chiral Lagrangian, |V_us|, the mass of the strange quark, and to simultaneously test the concept of quark-hadron duality. Using the best available measurements and a revisited analysis of the theoretical framework, the value alpha_s(m_tau) = 0.345 +- 0.004[exp] +- 0.009[theo] is obtained. Taken together with the determination of alpha_s(m_Z) from the global electroweak fit, this result leads to the most accurate test of asymptotic freedom: the value of the logarithmic slope of 1/alpha_s(s) is found to agree with QCD at a precision of 4%. In another approach, the tau spectral functions can be used to determine hadronic quantities that, due to the nonperturbative nature of long-distance QCD, cannot be computed from first principles. An example for this is the contribution from hadronic vacuum polarization to loop-dominated processes like the anomalous magnetic moment of the muon. This article reviews the measurements of nonstrange and strange tau spectral functions and their phenomenological applications.Comment: 89 pages, 32 figures; final version accepted for publication by Reviews of Modern Physic

Genome-wide association mapping for root traits in a panel of rice accessions from Vietnam

Background: Despite recent sequencing efforts, local genetic resources remain underexploited, even though they carry alleles that can bring agronomic benefits. Taking advantage of the recent genotyping with 22,000 single-nucleotide polymorphism markers of a core collection of 180 Vietnamese rice varieties originating from provinces from North to South Vietnam and from different agrosystems characterized by contrasted water regimes, we have performed a genome-wide association study for different root parameters. Roots contribute to water stress avoidance and are a still underexploited target for breeding purpose due to the difficulty to observe them. Results: The panel of 180 rice varieties was phenotyped under greenhouse conditions for several root traits in an experimental design with 3 replicates. The phenotyping system consisted of long plastic bags that were filled with sand and supplemented with fertilizer. Root length, root mass in different layers, root thickness, and the number of crown roots, as well as several derived root parameters and shoot traits, were recorded. The results were submitted to association mapping using a mixed model involving structure and kinship to enable the identification of significant associations. The analyses were conducted successively on the whole panel and on its indica (115 accessions) and japonica (64 accessions) subcomponents. The two associations with the highest significance were for root thickness on chromosome 2 and for crown root number on chromosome 11. No common associations were detected between the indica and japonica subpanels, probably because of the polymorphism repartition between the subspecies. Based on orthology with Arabidopsis, the possible candidate genes underlying the quantitative trait loci are reviewed. Conclusions: Some of the major quantitative trait loci we detected through this genome-wide association study contain promising candidate genes encoding regulatory elements of known key regulators of root formation and development