Learning soft task priorities for control of redundant robots

Movement primitives (MPs) provide a powerful framework for data driven movement generation that has been successfully applied for learning from demonstrations and robot reinforcement learning. In robotics we often want to solve a multitude of different, but related tasks. As the parameters of the primitives are typically high dimensional, a common practice for the generalization of movement primitives to new tasks is to adapt only a small set of control variables, also called meta parameters, of the primitive. Yet, for most MP representations, the encoding of these control variables is precoded in the representation and can not be adapted to the considered tasks. In this paper, we want to learn the encoding of task-specific control variables also from data instead of relying on fixed meta-parameter representations. We use hierarchical Bayesian models (HBMs) to estimate a low dimensional latent variable model for probabilistic movement primitives (ProMPs), which is a recent movement primitive representation. We show on two real robot datasets that ProMPs based on HBMs outperform standard ProMPs in terms of generalization and learning from a small amount of data and also allows for an intuitive analysis of the movement. We also extend our HBM by a mixture model, such that we can model different movement types in the same dataset

Caratterizzazione dell’ambiente marino dei Campi Flegrei. Risultati preliminari della campagna oceanografica RICAMAR 2013

The caldera of the Phlegraean Fields (also known as Campi Flegrei) is one of the most dangerous and populated volcanic area in the world, covering an area that comprises the western part of Naples and the Gulf of Pozzuoli. The main peculiarity of current volcanic activity is the gradual and periodic lift (positive or negative) of part of the Earth\u27s surface (bradyseism) combined, only during the positive phase, with a strong sismicity and surficial hydrotermal activity. Deformative models, calibrated using land-based measurements, highlighted the Gulf of Pozzuoli as the area with the largest deformation. Although the network of monitoring sensors on land is well developed and structured, there is a lack of sensing systems for the marine deformation. The activities of RIlievi per la Caratterizzazione dell’Ambiente MARino nel Golfo di Pozzuoli 2013 (RICAMAR2013) project - sinergically conducted by the Italian Navy\u27s Survey Vessel Ammiraglio Magnaghi , the Italian Hydrographic Office (IIM) and the Istituto Nazionale di Geofisica e Vulcanologia (INGV)- were targeted to fulfill this gap. In fact, the creation of marine observatories about the caldera\u27s phenomena will be based on the data collected during these bathymetric, magnetometric, stratigrafic and hydrologic surveys

Attentive Learning of Sequential Handwriting Movements: A Neural Network Model

Defense Advanced research Projects Agency and the Office of Naval Research (N00014-95-1-0409, N00014-92-J-1309); National Science Foundation (IRI-97-20333); National Institutes of Health (I-R29-DC02952-01)

Everybody hurts: a survey on mental health in academia.

The impacts of the pandemic were multiple: health, social, economic, political, on quality of life and well-being. The aim of this project is to attempt to study the mental health status, the problems of which are constantly increasing, of Italian academic social scientists experiencing difficulties during the pandemic period, as they play a relevant role within society, trying to construct an index identifying their level of malaise using exploratory factor analysis and logistic regression

Conclusions on motor control depend on the type of model used to represent the periphery

Within the field of motor control, there is no consensus on which kinematic and kinetic aspects of movements are planned or controlled. Perturbing goal-directed movements is a frequently used tool to answer this question. To be able to draw conclusions about motor control from kinematic responses to perturbations, a model of the periphery (i.e., the skeleton, muscle-tendon complexes, and spinal reflex circuitry) is required. The purpose of the present study was to determine to what extent such conclusions depend on the level of simplification with which the dynamical properties of the periphery are modeled. For this purpose, we simulated fast goal-directed single-joint movement with four existing types of models. We tested how three types of perturbations affected movement trajectory if motor commands remained unchanged. We found that the four types of models of the periphery showed different robustness to the perturbations, leading to different predictions on how accurate motor commands need to be, i.e., how accurate the knowledge of external conditions needs to be. This means that when interpreting kinematic responses obtained in perturbation experiments the level of error correction attributed to adaptation of motor commands depends on the type of model used to describe the periphery

Deciphering the functional role of spatial and temporal muscle synergies in whole-body movements

International audienceVoluntary movement is hypothesized to rely on a limited number of muscle synergies, the recruitment of which translates task goals into effective muscle activity. In this study, we investigated how to analytically characterize the functional role of different types of muscle synergies in task performance. To this end, we recorded a comprehensive dataset of muscle activity during a variety of whole-body pointing movements. We decomposed the electromyographic (EMG) signals using a space-by-time modularity model which encompasses the main types of synergies. We then used a task decoding and information theoretic analysis to probe the role of each synergy by mapping it to specific task features. We found that the temporal and spatial aspects of the movements were encoded by different temporal and spatial muscle synergies, respectively, consistent with the intuition that there should a correspondence between major attributes of movement and major features of synergies. This approach led to the development of a novel computational method for comparing muscle synergies from different participants according to their functional role. This functional similarity analysis yielded a small set of temporal and spatial synergies that describes the main features of whole-body reaching movements

Chemoproteomics reveals Toll-like receptor fatty acylation

Partial funding for Open Access provided by The Ohio State University Open Access Fund.Background: Palmitoylation is a 16-carbon lipid post-translational modification that increases protein hydrophobicity. This form of protein fatty acylation is emerging as a critical regulatory modification for multiple aspects of cellular interactions and signaling. Despite recent advances in the development of chemical tools for the rapid identification and visualization of palmitoylated proteins, the palmitoyl proteome has not been fully defined. Here we sought to identify and compare the palmitoylated proteins in murine fibroblasts and dendritic cells. Results: A total of 563 putative palmitoylation substrates were identified, more than 200 of which have not been previously suggested to be palmitoylated in past proteomic studies. Here we validate the palmitoylation of several new proteins including Toll-like receptors (TLRs) 2, 5 and 10, CD80, CD86, and NEDD4. Palmitoylation of TLR2, which was uniquely identified in dendritic cells, was mapped to a transmembrane domain-proximal cysteine. Inhibition of TLR2 S-palmitoylation pharmacologically or by cysteine mutagenesis led to decreased cell surface expression and a decreased inflammatory response to microbial ligands. Conclusions: This work identifies many fatty acylated proteins involved in fundamental cellular processes as well as cell type-specific functions, highlighting the value of examining the palmitoyl proteomes of multiple cell types. Spalmitoylation of TLR2 is a previously unknown immunoregulatory mechanism that represents an entirely novel avenue for modulation of TLR2 inflammatory activity.This work was supported by funding from the NIH/NIAID (grant R00AI095348 to J.S.Y.), the NIH/NIGMS (R01GM087544 to HCH), and the Ohio State University Public Health Preparedness for Infectious Diseases (PHPID) program. NMC is supported by the Ohio State University Systems and Integrative Biology Training Program (NIH/NIGMS grant T32GM068412). BWZ is a fellow of the National Science Foundation Graduate Research Fellowship Program (DGE-0937362)

Land Use Regression Models for Ultrafine Particles in Six European Areas

Long-term ultrafine particle (UFP) exposure estimates at a fine spatial scale are needed for epidemiological studies. Land use regression (LUR) models were developed and evaluated for six European areas based on repeated 30 min monitoring following standardized protocols. In each area; Basel (Switzerland), Heraklion (Greece), Amsterdam, Maastricht, and Utrecht ("The Netherlands"), Norwich (United Kingdom), Sabadell (Spain), and Turin (Italy), 160-240 sites were monitored to develop LUR models by supervised stepwise selection of GIS predictors. For each area and all areas combined, 10 models were developed in stratified random selections of 90% of sites. UFP prediction robustness was evaluated with the intraclass correlation coefficient (ICC) at 31-50 external sites per area. Models from Basel and The Netherlands were validated against repeated 24 h outdoor measurements. Structure and model R2 of local models were similar within, but varied between areas (e.g., 38-43% Turin; 25-31% Sabadell). Robustness of predictions within areas was high (ICC 0.73-0.98). External validation R2 was 53% in Basel and 50% in The Netherlands. Combined area models were robust (ICC 0.93-1.00) and explained UFP variation almost equally well as local models. In conclusion, robust UFP LUR models could be developed on short-term monitoring, explaining around 50% of spatial variance in longer-term measurements

A model of open-loop control of equilibrium position and stiffness of the human elbow joint

According to the equilibrium point theory, the control of posture and movement involves the setting of equilibrium joint positions (EP) and the independent modulation of stiffness. One model of EP control, the α-model, posits that stable EPs and stiffness are set open-loop, i.e. without the aid of feedback. The purpose of the present study was to explore for the elbow joint the range over which stable EPs can be set open-loop and to investigate the effect of co-contraction on intrinsic low-frequency elbow joint stiffness (