Combining Physical Simulators and Object-Based Networks for Control

Physics engines play an important role in robot planning and control; however, many real-world control problems involve complex contact dynamics that cannot be characterized analytically. Most physics engines therefore employ . approximations that lead to a loss in precision. In this paper, we propose a hybrid dynamics model, simulator-augmented interaction networks (SAIN), combining a physics engine with an object-based neural network for dynamics modeling. Compared with existing models that are purely analytical or purely data-driven, our hybrid model captures the dynamics of interacting objects in a more accurate and data-efficient manner.Experiments both in simulation and on a real robot suggest that it also leads to better performance when used in complex control tasks. Finally, we show that our model generalizes to novel environments with varying object shapes and materials.Comment: ICRA 2019; Project page: http://sain.csail.mit.ed

The Intermediate Line Region and the Baldwin Effect

Statistical investigations of samples of quasars have established that clusters of properties are correlated. The strongest trends among the ultraviolet emission-line properties are characterized by the object-to-object variation of emission from low-velocity gas, the so-called ``intermediate-line region'' or ILR. The strongest trends among the optical emission-line properties are characterized by the object-to-object variation of the line intensity ratio of [O III] 5007 to optical Fe II. Additionally, the strength of ILR emission correlates with [O III]/Fe II, as well as with radio and X-ray properties. The fundamental physical parameter driving these related correlations is not yet identified. Because the variation in the ILR dominates the variation in the equivalent widths of lines showing the Baldwin effect, it is important to understand whether the physical parameter underlying this variation also drives the Baldwin effect or is a primary source of scatter in the Baldwin effect.Comment: 11 pages, to appear in the proceedings of the meeting on "Quasars as Standard Candles for Cosmology" held on May 18-22, 1998, at La Serena, Chile. To be published by ASP, editor G. Ferlan

Unsharp Values, Domains and Topoi

The so-called topos approach provides a radical reformulation of quantum theory. Structurally, quantum theory in the topos formulation is very similar to classical physics. There is a state object, analogous to the state space of a classical system, and a quantity-value object, generalising the real numbers. Physical quantities are maps from the state object to the quantity-value object -- hence the `values' of physical quantities are not just real numbers in this formalism. Rather, they are families of real intervals, interpreted as `unsharp values'. We will motivate and explain these aspects of the topos approach and show that the structure of the quantity-value object can be analysed using tools from domain theory, a branch of order theory that originated in theoretical computer science. Moreover, the base category of the topos associated with a quantum system turns out to be a domain if the underlying von Neumann algebra is a matrix algebra. For general algebras, the base category still is a highly structured poset. This gives a connection between the topos approach, noncommutative operator algebras and domain theory. In an outlook, we present some early ideas on how domains may become useful in the search for new models of (quantum) space and space-time.Comment: 32 pages, no figures; to appear in Proceedings of Quantum Field Theory and Gravity, Regensburg (2010

Minimising the heat dissipation of quantum information erasure

Quantum state engineering and quantum computation rely on information erasure procedures that, up to some fidelity, prepare a quantum object in a pure state. Such processes occur within Landauer's framework if they rely on an interaction between the object and a thermal reservoir. Landauer's principle dictates that this must dissipate a minimum quantity of heat, proportional to the entropy reduction that is incurred by the object, to the thermal reservoir. However, this lower bound is only reachable for some specific physical situations, and it is not necessarily achievable for any given reservoir. The main task of our work can be stated as the minimisation of heat dissipation given probabilistic information erasure, i.e., minimising the amount of energy transferred to the thermal reservoir as heat if we require that the probability of preparing the object in a specific pure state $|\varphi_1\rangle$ be no smaller than $p_{\varphi_1}^{\max}-\delta$. Here $p_{\varphi_1}^{\max}$ is the maximum probability of information erasure that is permissible by the physical context, and $\delta\geqslant 0$ the error. To determine the achievable minimal heat dissipation of quantum information erasure within a given physical context, we explicitly optimise over all possible unitary operators that act on the composite system of object and reservoir. Specifically, we characterise the equivalence class of such optimal unitary operators, using tools from majorisation theory, when we are restricted to finite-dimensional Hilbert spaces. Furthermore, we discuss how pure state preparation processes could be achieved with a smaller heat cost than Landauer's limit, by operating outside of Landauer's framework

Newtonian Image Understanding: Unfolding the Dynamics of Objects in Static Images

In this paper, we study the challenging problem of predicting the dynamics of objects in static images. Given a query object in an image, our goal is to provide a physical understanding of the object in terms of the forces acting upon it and its long term motion as response to those forces. Direct and explicit estimation of the forces and the motion of objects from a single image is extremely challenging. We define intermediate physical abstractions called Newtonian scenarios and introduce Newtonian Neural Network ($N^3$) that learns to map a single image to a state in a Newtonian scenario. Our experimental evaluations show that our method can reliably predict dynamics of a query object from a single image. In addition, our approach can provide physical reasoning that supports the predicted dynamics in terms of velocity and force vectors. To spur research in this direction we compiled Visual Newtonian Dynamics (VIND) dataset that includes 6806 videos aligned with Newtonian scenarios represented using game engines, and 4516 still images with their ground truth dynamics

Phenomenal regression to the real object in physical and virtual worlds

© 2014, Springer-Verlag London. In this paper, we investigate a new approach to comparing physical and virtual size and depth percepts that captures the involuntary responses of participants to different stimuli in their field of view, rather than relying on their skill at judging size, reaching or directed walking. We show, via an effect first observed in the 1930s, that participants asked to equate the perspective projections of disc objects at different distances make a systematic error that is both individual in its extent and comparable in the particular physical and virtual setting we have tested. Prior work has shown that this systematic error is difficult to correct, even when participants are knowledgeable of its likelihood of occurring. In fact, in the real world, the error only reduces as the available cues to depth are artificially reduced. This makes the effect we describe a potentially powerful, intrinsic measure of VE quality that ultimately may contribute to our understanding of VE depth compression phenomena