Understanding physical relations between objects, especially their support relations, is crucial for robotic manipulation. There has been work on reasoning about support relations and structural stability of simple configurations in RGB-D images. In this paper, we propose a method for extracting more detailed physical knowledge from a set of RGB-D images taken from the same scene but from different views using qualitative reasoning and intuitive physical models. Rather than providing a simple contact relation graph and approximating stability over convex shapes, our method is able to provide a detailed supporting relation analysis based on a volumetric representation. Specifically, true supporting relations between objects (e.g., if an object supports another object by touching it on the side or if the object above contributes to the stability of the object below) are identified. We apply our method to real-world structures captured in warehouse scenarios and show our method works as desired.
Models for predicting stability of a structure have been studied for many decades. Fahlman @cite_1 proposed a model to analyze system stability based on Newton's Laws. Simulation based models were also presented in recent years @cite_22 @cite_13 . However, @cite_18 argues that probabilistic simulation based methods are not suitable for automatic physical reasoning due to some limitations including the lack of capability to handle imprecise input. Thus in our approach, we aim to apply qualitative spatial reasoning to combine raw information from multiple views to extract understandable and more precise relations between objects in the environment.
@cite_13 @cite_18 @cite_1 @cite_22
2586029550 2214227108 2147072697 2051466908
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. 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 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 at run-time. 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. We first evaluate the approach on synthetic data and compared the results to human judgments on the same stimuli. Further, we extend this approach to reason about future states of such towers that in return enables successful stacking. In scientific computing and in realistic graphic animation, simulation - that is, step-by-step calculation of the complete trajectory of a physical system - is one of the most common and important modes of calculation. In this article, we address the scope and limits of the use of simulation, with respect to AI tasks that involve high-level physical reasoning. We argue that, in many cases, simulation can play at most a limited role. Simulation is most effective when the task is prediction, when complete information is available, when a reasonably high quality theory is available, and when the range of scales involved, both temporal and spatial, is not extreme. When these conditions do not hold, simulation is less effective or entirely inappropriate. We discuss twelve features of physical reasoning problems that pose challenges for simulation-based reasoning. We briefly survey alternative techniques for physical reasoning that do not rely on simulation. This paper describes BUILD, a computer program which generates plans for building specified structures out of simple objects such as toy blocks. A powerful heuristic control structure enables BUILD to use a number of sophisticated construction techniques in its plans. Among these are the incorporation of pre-existing structure into the final design, pre-assembly of movable sub-structures on the table, and the use of extra blocks as temporary supports and counterweights in the course of the construction. BUILD does its planning in a modeled 3-space in which blocks of various shapes and sizes can be represented in any orientation and location. The modeling system can maintain several world models at once, and contains modules for displaying states, testing them for inter-object contact and collision, and for checking the stability of complex structures involving frictional forces. Suggestions are included for the extension of BUILD-like systems to other domains. Also discussed are the merits of BUILD's implementation language, conniver, for this type of problem solving. Visual estimation of object stability is an ecologically important judgment that allows observers to predict the physical behavior of objects. A natural method that has been used in previous work to measure perceived object stability is the estimation of perceived “critical angle”—the angle at which an object appears equally likely to fall over versus return to its upright stable position. For an asymmetric object, however, the critical angle is not a single value, but varies with the direction in which the object is tilted. The current study addressed two questions: (a) Can observers reliably track the change in critical angle as a function of tilt direction? (b) How do they visually estimate the overall stability of an object, given the different critical angles in various directions? To address these questions, we employed two experimental tasks using simple asymmetric 3D objects (skewed conical frustums): settings of critical angle in different directions relative to the intrinsic skew of the 3D object (Experiment 1), and stability matching across 3D objects with different shapes (Experiments 2 and 3). Our results showed that (a) observers can perceptually track the varying critical angle in different directions quite well; and (b) their estimates of overall object stability are strongly biased toward the minimum critical angle (i.e., the critical angle in the least stable direction). Moreover, the fact that observers can reliably match perceived object stability across 3D objects with different shapes suggests that perceived stability is likely to be represented along a single dimension.