From Demonstrations to Task-Space Specifications:Using Causal Analysis to Extract Rule Parameterization from Demonstrations

Learning models of user behaviour is an important problem that is broadly applicable across many application domains requiring human-robot interaction. In this work, we show that it is possible to learn generative models for distinct user behavioural types, extracted from human demonstrations, by enforcing clustering of preferred task solutions within the latent space. We use these models to differentiate between user types and to find cases with overlapping solutions. Moreover, we can alter an initially guessed solution to satisfy the preferences that constitute a particular user type by backpropagating through the learned differentiable models. An advantage of structuring generative models in this way is that we can extract causal relationships between symbols that might form part of the user's specification of the task, as manifested in the demonstrations. We further parameterize these specifications through constraint optimization in order to find a safety envelope under which motion planning can be performed. We show that the proposed method is capable of correctly distinguishing between three user types, who differ in degrees of cautiousness in their motion, while performing the task of moving objects with a kinesthetically driven robot in a tabletop environment. Our method successfully identifies the correct type, within the specified time, in 99% [97.8 - 99.8] of the cases, which outperforms an IRL baseline. We also show that our proposed method correctly changes a default trajectory to one satisfying a particular user specification even with unseen objects. The resulting trajectory is shown to be directly implementable on a PR2 humanoid robot completing the same task.Comment: arXiv admin note: substantial text overlap with arXiv:1903.0126

Afrika’s bevolkingsdynamiek

De bevolking van het Afrikaanse continent is de laatste vijftig jaar extreem snel gegroeid, van 289 miljoen inwoners in 1961 tot meer dan 1 miljard op dit moment. Dat is een groei van 350% in een halve eeuw. Het aantal stedelingen is nog veel sneller gegroeid: van 65 miljoen (20%) in 1960 tot 460 miljoen (46%) nu, en spoedig, zo is de verwachting van demografen, zal meer dan 50% van alle Afrikanen in een stad wonen. De gemiddelde levensverwachting in Afrika, de alfabetisatiegraad en de deelname aan het lager onderwijs zijn ook allemaal spectaculair gestegen. En er zijn nu relatief heel veel gezonde, redelijk goed opgeleide jongeren met een sterk toegenomen 'blik naar buiten'.De veranderingen in de bevolkingsopbouw van Afrika beginnen zichtbaar te worden in de bevolkingspiramides. Voor zuidelijk Afrika is al een echte 'jongerenbult' (youth bulge) te zien, waarbij de leeftijdsgroepen van 10 tot 30 jaar groter zijn dan die van de 0-10 jarigen en van de groepen boven de 30. Voor de andere delen van Afrika en voor Afrika als geheel is het nog steeds zo dat de gra?ek nog de vorm heeft van een piramide en nog niet die van een ui, maar de verwachting van demografen is dat in de komende decennia ook voor overig Afrika sprake zal zijn van een afnemend geboorteaantal en dus van een structuurverandering in de bevolking. Ook voor die delen van Afrika geldt overigens al dat er relatief erg veel 10-30-jarigen zijn, vergeleken bij alle andere delen van de huidige wereldbevolking. Het feit dat er relatief zo veel 10-30-jarigen zijn en dat zovelen van hen nu aanzienlijk beter opgeleid zijn dan hun ouders geeft een grote druk op de arbeidsmarkt, waarbij veel jongeren teleurgesteld constateren dat ze ondanks hun onderwijsniveau geen toegang lijken te krijgen tot een grotere welvaart en tot een beter leven

Africa population dynamics

Africa's population has grown extremely rapidly over the last fifty years from 289 million inhabitants in 1961 to more than 1 billion today. This is a growth rate of 350% in just half a century and the number of urban residents has increased even more quickly: from 65 million in 1960 to 460 million today, or from 20% to 46% of the population as a whole. Demographers predict that soon more than 50% of all Africans will be living in cities. The average life expectancy, literacy rates and primary-school attendance figures in Africa have also all increased spectacularly. And today there are large numbers of relatively healthy, well-educated young people with a more international view of the world. Changes in the structure of Africa's population are evident in the continent's population pyramid. A 'youth bulge' can be seen in Southern Africa's population statistics, with those aged between 10 and 30 far outnumbering those in the 0-10 age group and those over 30 years of age. In other parts of Africa, and for Africa as a whole, the population statistics still have a pyramid structure and have not yet created the sort of onion shape seen in Southern Africa. The expectation is, however, that a declining birth rate across Africa over the next few decades will lead to a change in the continent's population structure and there will be a relatively high number of 10-30 year olds compared to other parts of the world. The fact that there are so many youth in Africa today and that they are much better educated than their parents ever were is having a big impact on the labour market. The youth are starting to feel disillusioned about the lack of job opportunities and are realizing that their (reasonably good) level of education is not going to allow them direct or easy access to greater prosperity and a better life.</p

High resolution and analytical transmission electron microscopy in a liquid flow cell via gas purging

Liquid phase electron microscopy (LPEM) based on sandwiched MEMS sample carriers provides the means toobserve time-resolved dynamics in a liquid state. Until now, LPEM has been widely used in materials science,energy and life science, providing fundamental insights into nucleation and growth, the dynamical evolution ofkey materials in batteries and fuel cells, as well as the 3D imaging of biomolecules [1]. Compared to liquid cellswithout a flowing function (such as static graphene pocket cells), liquid flow cells have obvious advantages.This includes the control of the liquid environment, the modulation of the effect of electron beam irradiation [2]and the integration of functional electrodes for heating or/and biasing. Due to the pressure difference betweenthe TEM column (~ 0 bar) and the enclosed liquid cell (~1 bar), the two membranes (silicon nitride with atypical thickness of ~50 nm) bulge outwards, resulting in a thick liquid layer, which can reach more than 1micrometer in the cell center region. Therefore, performing high resolution and analytical electron microscopystudies in a liquid flow cell comes with a multitude of challenges.Several strategies have been proposed to solve this issue, including (1) decreasing the membrane thickness orreplacing it with ultrathin materials e.g. graphene, h-BN, MoS2, etc. [3], (2) developing novel cellconfigurations, namely hole array patterns [4] and nanochannel [5], to avoid or decrease the bulging, (3)generating a gas bubble via electron beam irradiation [6,7], (4) generating a gas bubble via electrochemicalwater splitting [8] and (5) mitigating the window´s bulging by changing the pressure difference between the celland TEM column, either via an external pressure controller [9,10] or via the internal Laplace pressure [10].Those methods have been proven useful in high resolution and analytical electron microscopy studies in LPEM,however, there are also intrinsic limitations in each method.In this work, we propose a general and robust method to perform high resolution and analytical electronmicroscopy studies in a flow cell (the Stream Nano-Cell), which can be implemented during liquid heating orliquid biasing experiments. Thanks to the on-chip flow channel of the Stream Nano-Cell [11], the liquid in thefield of view can be removed by flowing gas (including inert gases to avoid problems with air sensitivity), whichis termed "purging". This purging method enables the acquisition of high-resolution TEM images, chemicalcomposition and valence analysis through energy-dispersive X-ray spectroscopy (EDX) mapping and ElectronEnergy-Loss Spectroscopy (EELS), respectively. In addition, the purging approach is both reversible andreproducible, which therefore enables the alternation between a full cell and a thin liquid configuration to studyliquid-thickness-dependent physical and chemical phenomena.References1.F. M. Ross. Science, 2015, 350, aaa9886.2.N. M. Schneider, et al. J. Phys. Chem. C, 2014, 118, 22373.3.G. Dunn, et al., ACS Nano, 2020, 14, 9637.4.S. Nagashima, et al. Nano Lett., 2019, 19, 10, 7000.5.M. N. Yesibolati, et al. Phys. Rev. Lett., 2020, 124, 065502.6.G. Zhu, et al. Chem. Commun. 2013, 49, 10944.7.U. Mirsaidov, et al. Soft Matter, 2012, 8, 7108.8.R. Serra-Maia, et al. ACS Nano 2021, 15, 10228.9.S. Keskin, et al. Nano Lett., 2019, 19, 4608.10.H. Wu, et al. Small Methods, 2021, 5, 2001287.11.A. F. Beker, et al. Nanoscale, 2020, 12, 22192