Reasoning About Liquids via Closed-Loop Simulation

Simulators are powerful tools for reasoning about a robot's interactions with its environment. However, when simulations diverge from reality, that reasoning becomes less useful. In this paper, we show how to close the loop between liquid simulation and real-time perception. We use observations of liquids to correct errors when tracking the liquid's state in a simulator. Our results show that closed-loop simulation is an effective way to prevent large divergence between the simulated and real liquid states. As a direct consequence of this, our method can enable reasoning about liquids that would otherwise be infeasible due to large divergences, such as reasoning about occluded liquid.Comment: Robotics: Science & Systems (RSS), July 12-16, 2017. Cambridge, MA, US

Amorphous silica between confining walls and under shear: a computer simulation study

Molecular dynamics computer simulations are used to investigate a silica melt confined between walls at equilibrium and in a steady-state Poisseuille flow. The walls consist of point particles forming a rigid face-centered cubic lattice and the interaction of the walls with the melt atoms is modelled such that the wall particles have only a weak bonding to those in the melt, i.e. much weaker than the covalent bonding of a Si-O unit. We observe a pronounced layering of the melt near the walls. This layering, as seen in the total density profile, has a very irregular character which can be attributed to a preferred orientational ordering of SiO4 tetrahedra near the wall. On intermediate length scales, the structure of the melt at the walls can be well distinguished from that of the bulk by means of the ring size distribution. Whereas essentially no structural changes occur in the bulk under the influence of the shear fields considered, strong structural rearrangements in the ring size distribution are present at the walls as far as there is a slip motion. For the sheared system, parabolic velocity profiles are found in the bulk region as expected from hydrodynamics and the values for the shear viscosity as extracted from those profiles are in good agreement with those obtained in pure bulk simulations from the appropriate Green-Kubo formula.Comment: 23 pages of Late

Supercooled Liquids Under Shear: Theory and Simulation

We analyze the behavior of supercooled fluids under shear both theoretically and numerically. Theoretically, we generalize the mode-coupling theory of supercooled fluids to systems under stationary shear flow. Our starting point is the set of generalized fluctuating hydrodynamic equations with a convection term. A nonlinear integro-differential equation for the intermediate scattering function is constructed. This theory is applied to a two-dimensional colloidal suspension. The shear rate dependence of the intermediate scattering function and the shear viscosity is analyzed. We have also performed extensive numerical simulations of a two-dimensional binary liquid with soft-core interactions near, but above, the glass transition temperature. Both theoretical and numerical results show: (i) A drastic reduction of the structural relaxation time and the shear viscosity due to shear. Both the structural relaxation time and the viscosity decrease as $\dot{\gamma}^{-\nu}$ with an exponent $\nu \leq 1$, where $\dot{\gamma}$ is the shear rate. (ii) Almost isotropic dynamics regardless of the strength of the anisotropic shear flow.Comment: 14 pages, 14 figure

To Stir or Not to Stir:Online Estimation of Liquid Properties for Pouring Actions

Our brains are able to exploit coarse physical models of fluids to solve everyday manipulation tasks. There has been considerable interest in developing such a capability in robots so that they can autonomously manipulate fluids adapting to different conditions. In this paper, we investigate the problem of adaptation to liquids with different characteristics. We develop a simple calibration task (stirring with a stick) that enables rapid inference of the parameters of the liquid from RBG data. We perform the inference in the space of simulation parameters rather than on physically accurate parameters. This facilitates prediction and optimization tasks since the inferred parameters may be fed directly to the simulator. We demonstrate that our "stirring" learner performs better than when the robot is calibrated with pouring actions. We show that our method is able to infer properties of three different liquids -- water, glycerin and gel -- and present experimental results by executing stirring and pouring actions on a UR10. We believe that decoupling of the training actions from the goal task is an important step towards simple, autonomous learning of the behavior of different fluids in unstructured environments.Comment: Presented at the Modeling the Physical World: Perception, Learning, and Control Workshop (NeurIPS) 201