This paper proposes a novel hierarchical compositional representation of 3D shape that can accommodate a large number of object categories and enables efficient learning and inference. The hierarchy starts with simple pre-defined parts on the first layer, after which subsequent layers are learned recursively by taking the most statistically significant compositions of parts from the previous layer. Our representation is able to scale because of its very economical use of memory and because subparts of the representation are shared. We apply our representation to 3D multi-class object categorization. Object categories are represented by histograms of compositional parts, which are then used as inputs to an SVM classifier. We present results for two datasets, Aim Shape [1] and the Washington RGB-D Object Dataset [2], and demonstrate the competitive performance of our method.

Predicting the motions of rigid objects under contacts is a necessary precursor to planning of robot manipulation of objects. On the one hand physics based rigid body simulations are used, and on the other learning approaches are being developed. The advantage of physics simulations is that because they explicitly perform collision checking they respect kinematic constraints, producing physically plausible predictions. The advantage of learning approaches is that they can capture the effects on motion of unobservable parameters such as mass distribution, and frictional coefficients, thus producing more accurate predicted trajectories. This paper shows how to bring together the advantages of both approaches to achieve learned simulators of specific objects that outperform previous learning approaches. Our approach employs a fast simplified collision checker and a learning method. The learner predicts trajectories for the object. These are optimised post prediction to minimise interpenetrations according to the collision checker. In addition we show that cleaning the training data prior to learning can also improve performance. Combining both approaches results in consistently strong prediction performance. The new simulator outperforms previous learning based approaches on a single contact push manipulation prediction task. We also present results showing that the method works for multi-contact manipulation, for which rigid body simulators are notoriously unstable.

An important problem in robotic manipulation is the ability to predict how objects behave under manipulative actions. This ability is necessary to allow planning of object manipulations. Physics simulators can be used to do this, but they model many kinds of object interaction poorly. An alternative is to learn a motion model for objects by interacting with them. In this paper we address the problem of learning to predict the interactions of rigid bodies in a probabilistic framework, and demonstrate the results in the domain of robotic push manipulation. A robot arm applies random pushes to various objects and observes the resulting motion with a vision system. The relationship between push actions and object motions is learned, and enables the robot to predict the motions that will result from new pushes. The learning does not make explicit use of physics knowledge, or any pre-coded physical constraints, nor is it even restricted to domains which obey any particular rules of physics. We use regression to learn efficiently how to predict the gross motion of a particular object. We further show how different density functions can encode different kinds of information about the behaviour of interacting objects. By combining these as a product of densities, we show how learned predictors can cope with a degree of generalisation to previously unencountered object shapes, subjected to previously unencountered push directions. Performance is evaluated through a combination of virtual experiments in a physics simulator, and real experiments with a 5-axis arm equipped with a simple, rigid finger.

Visual tracking of an object can provide a powerful source of feedback information during complex robotic manipulation operations, especially those in which there may be uncertainty about which new object pose may result from a planned manipulative action. At the same time, robotic manipulation can provide a challenging environment for visual tracking, with occlusions of the object by other objects or by the robot itself, and sudden changes in object pose that may be accompanied by motion blur. Recursive filtering techniques use motion models for predictor-corrector tracking, but the simple models typically used often fail to adequately predict the complex motions of manipulated objects. We show how statistical machine learning techniques can be used to train sophisticated motion predictors, which incorporate additional information by being conditioned on the planned manipulative action being executed. We then show how these learned predictors can be used to propagate the particles of a particle filter from one predictor-corrector step to the next, enabling a visual tracking algorithm to maintain plausible hypotheses about the location of an object, even during severe occlusion and other difficult conditions. We demonstrate the approach in the context of robotic push manipulation, where a 5-axis robot arm equipped with a rigid finger applies a series of pushes to an object, while it is tracked by a vision algorithm using a single camera.