Learning Manipulation under Physics Constraints with Visual Perception

Understanding physical phenomena is a key competence that enables humans and animals to act and interact under uncertain perception in previously unseen environments containing novel objects and their configurations. In this work, we consider the problem of autonomous block stacking and explore solutions to learning manipulation under physics constraints with visual perception inherent to the task. Inspired by the intuitive physics in humans, we first present an end-to-end learning-based approach to predict stability directly from appearance, contrasting a more traditional model-based approach with explicit 3D representations and physical simulation. We study the model's behavior together with an accompanied human subject test. It is then integrated into a real-world robotic system to guide the placement of a single wood block into the scene without collapsing existing tower structure. To further automate the process of consecutive blocks stacking, we present an alternative approach where the model learns the physics constraint through the interaction with the environment, bypassing the dedicated physics learning as in the former part of this work. In particular, we are interested in the type of tasks that require the agent to reach a given goal state that may be different for every new trial. Thereby we propose a deep reinforcement learning framework that learns policies for stacking tasks which are parametrized by a target structure

Method of fabricating a photovoltaic module of a substantially transparent construction

A method characterized by the steps of positioning a plurality of uniformly dimensioned photovoltaic cells in registered relation with a plurality of openings formed in a planar tool is disclosed. The method allows acess to the P contact surface of each of the cells. The steps of the method are: (1) connecting the N contact surface of alternate cells to the P contact surface of the cells interposed therebetween, (2) removing therefrom residue of solder flux, (3) applying to the N contact surfaces of the cells a transparent adhesive, (4) placing a common transparent cover plate in engaged relation with the adhesive, (5) placing a film over the circular openings for hermetically sealing the openings, and (6) establishing a vacuum between the film and the cover plate

Protons as the prime contributors to the storm time ring current

Following a large magnetic storm (17 June 1972), Explorer 45 measured the equatorial particle populations and magnetic field. Using data obtained during the symmetic recovery phase, it is shown that through a series of self-consistent calculations, the measured protons with energies from 1 to 872 keV, can account for the observed ring current magnetic effects within experimental uncertainities. This enables an upper limit to be set for the heavy ion contribution to the storm time ring current

Optical properties of an electron bound to a double well potential in a dielectric

The absorption and emission properties of F2+ center in KCl (2 adjacent anion vacancies which bind 1 electron) were investigated between 0.6 and 5 eV by using an optically aligned center system. Energy positions, level degeneracies, polarization behavior and oscillator strengths of 8 absorption transitions were found in close agreement to the calculated states of the H2+ molecular ion corrected for a single dielectric constant and the proper separation between the 2 effective positive charges. Excitation of the lowest 2póu state at 1.38 ì leads with temperature independent quantum efficiency to a Stokes shifted emission at 1.67 ì almost in mirror symmetry with the zero phonon line at 1.5260 ì. Excitation of all higher absorption transitions (2pðu, etc.) at liquid helium temperature (LHeT) produces a visible luminescence at 600 nm. At higher temperature a thermally activated process (activation energy ÄE = 0.063 eV) transfers the excitation energy to the lowest excited state 2póu quenching the 600-nm luminescence and producing the IR emission

The distribution of silicate strength in Spitzer spectra of AGNs and ULIRGs

A sample of 196 AGNs and ULIRGs observed by the Infrared Spectrograph (IRS) on Spitzer is analyzed to study the distribution of the strength of the 9.7 micron silicate feature. Average spectra are derived for quasars, Seyfert 1 and Seyfert 2 AGNs, and ULIRGs. We find that quasars are characterized by silicate features in emission and Seyfert 1s equally by emission or weak absorption. Seyfert 2s are dominated by weak silicate absorption, and ULIRGs are characterized by strong silicate absorption (mean apparent optical depth about 1.5). Luminosity distributions show that luminosities at rest frame 5.5 micron are similar for the most luminous quasars and ULIRGs and are almost 10^5 times more luminous than the least luminous AGN in the sample. The distributions of spectral characteristics and luminosities are compared to those of optically faint infrared sources at z~2 being discovered by the IRS, which are also characterized by strong silicate absorption. It is found that local ULIRGs are a similar population, although they have lower luminosities and somewhat stronger absorption compared to the high redshift sources.Comment: Accepted for publication on ApJ

To Fall Or Not To Fall: {A} Visual Approach to Physical Stability Prediction

Understanding physical phenomena is a key competence that enables humans and animals to act and interact under uncertain perception in previously unseen environments containing novel object and their configurations. Developmental psychology has shown that such skills are acquired by infants from observations at a very early stage. In this paper, we contrast a more traditional approach of taking a model-based route with explicit 3D representations and physical simulation by an end-to-end approach that directly predicts stability and related quantities from appearance. We ask the question if and to what extent and quality such a skill can directly be acquired in a data-driven way bypassing the need for an explicit simulation. We present a learning-based approach based on simulated data that predicts stability of towers comprised of wooden blocks under different conditions and quantities related to the potential fall of the towers. The evaluation is carried out on synthetic data and compared to human judgments on the same stimuli