In this paper, we present a novel grasp selection algorithm to enable a robot with a two-fingered end-effector to autonomously grasp unknown objects. Our approach requires as input only the raw depth data obtained from a single frame of a 3D sensor. Additionally, our approach uses no explicit models of the objects and does not require a training phase. We use the grasping capability to demonstrate the application of a robot as an autonomous checkout clerk. To perform this task, the robot must identify how to grasp an object, locate the barcode on the object and read the numeric code. We evaluate our grasping algorithm in experiments where the robot was required to autonomously grasp unknown objects. The robot achieved a success of 91.6%in grasping novel objects. We performed two sets of experiments to evaluate the checkout robot application. In the first set, the objects were placed in many orientations in front of the robot one at a time. In the second set, the objects were placed several at a time with varying amounts of clutter. The robot was able to autonomously grasp and scan the objects in 49/50 of the single-object trials and 46/50 of the cluttered trials.

Markerless tracking of human pose is a hard yet relevant problem. In this paper, we derive an efficient filtering algorithm for tracking human pose using a stream of monocular depth images. The key idea is to combine an accurate generative model - which is achievable in this setting using programmable graphics hardware - with a discriminative model that provides data-driven evidence about body part locations. In each filter iteration, we apply a form of local model-based search that exploits the nature of the kinematic chain. As fast movements and occlusion can disrupt the local search, we utilize a set of discriminatively trained patch classifiers to detect body parts. We describe a novel algorithm for propagating this noisy evidence about body part locations up the kinematic chain using the unscented transform. The resulting distribution of body configurations allows us to reinitialize the model-based search. We provide extensive experimental results on 28 real-world sequences using automatic ground-truth annotations from a commercial motion capture system.

We deal with the problem of detecting and identifying body parts in depth images at video frame rates. Our solution involves a novel interest point detector for mesh and range data that is particularly well suited for analyzing human shape. The interest points, which are based on identifying geodesic extrema on the surface mesh, coincide with salient points of the body, which can be classified as, e.g., hand, foot or head using local shape descriptors. Our approach also provides a natural way of estimating a 3D orientation vector for a given interest point. This can be used to normalize the local shape descriptors to simplify the classification problem as well as to directly estimate the orientation of body parts in space. Experiments involving ground truth labels acquired via an active motion capture system show that our interest points in conjunction with a boosted patch classifier are significantly better in detecting body parts in depth images than state-of-the-art sliding-window based detectors.

Markov networks are commonly used in a wide variety of applications, ranging from computer vision, to natural language, to computational biology. In most current applications, even those that rely heavily on learned models, the structure of the Markov network is constructed by hand, due to the lack of effective algorithms for learning Markov network structure from data. In this paper, we provide a computationally efficient method for learning Markov network structure from data. Our method is based on the use of L1 regularization on the weights of the log-linear model, which has the effect of biasing the model towards solutions where many of the parameters are zero. This formulation converts the Markov network learning problem into a convex optimization problem in a continuous space, which can be solved using efficient gradient methods. A key issue in this setting is the (unavoidable) use of approximate inference, which can lead to errors in the gradient computation when the network structure is dense. Thus, we explore the use of different feature introduction schemes and compare their performance. We provide results for our method on synthetic data, and on two real world data sets: pixel values in the MNIST data, and genetic sequence variations in the human HapMap data. We show that our L1 -based method achieves considerably higher generalization performance than the more standard L2 -based method (a Gaussian parameter prior) or pure maximum-likelihood learning. We also show that we can learn MRF network structure at a computational cost that is not much greater than learning parameters alone, demonstrating the existence of a feasible method for this important problem. Undirected graphical models, such as Markov networks or log-linear models, have been used in an ever-growing variety of applications, including computer vision, natural language, computational biology, and more. However, as this modeling framework is used in increasingly more complex and less well-understood domains, the problem of selecting from the exponentially large space of possible network structures becomes of great importance. Including all of the possibly relevant interactions in the model generally leads to overfitting, and can also lead to difficulties in running inference over the network. Moreover, learning a "good" structure can be an important task in its own right, as it can provide insight about the underlying structure in the domain. Unfortunately, the problem of learning Markov networks remains a challenge. The key difficulty is that the maximum likelihood (ML) parameters of these networks have no analytic closed form; finding these parameters requires an iterative procedure (such as conjugate gradient [15] or BFGS [5]), where each iteration runs inference over the current model. This type of procedure is computationally expensive even for models where inference is tractable. The problem of structure learning is considerably harder. The dominant type of solution to this problem uses greedy local heuristic search, which incrementally modifies the model by adding and possibly deleting features.

We consider the problem of modeling a helicopter's dynamics based on state-action trajectories collected from it. The contribution of this paper is two-fold. First, we consider the linear models such as learned by C I F E R (the industry standard in helicopter identification), and show that the linear parameterization makes certain properties of dynamical systems, such as inertia, fundamentally difficult to capture. We propose an alternative, acceleration based, parameterization that does not suffer from this deficiency, and that can be learned as efficiently from data. Second, a Markov decision process model of a helicopter's dynamics would explicitly model only the one-step transitions, but we are often interested in a model's predictive performance over longer timescales. In this paper, we present an efficient algorithm for (approximately) minimizing the prediction error over long time scales. We present empirical results on two different helicopters. Although this work was motivated by the problem of modeling helicopters, the ideas presented here are general, and can be applied to modeling large classes of vehicular dynamics.