This article discusses the quadratization of Markov Logic Networks, which enables efficient approximate MAP computation by means of maximum flows. The procedure relies on a pseudo-Boolean representation of the model, and allows handling models of any order. The employed pseudo-Boolean representation can be used to identify problems that are guaranteed to be solvable in low polynomial-time. Results on common benchmark problems show that the proposed approach finds optimal assignments for most variables in excellent computational time and approximate solutions that match the quality of ILP-based solvers.

Most MAP inference algorithms for CRFs optimize an energy function knowing all the potentials. In this paper, we focus on CRFs where the computational cost of instantiating the potentials is orders of magnitude higher than MAP inference. This is often the case in semantic image segmentation, where most potentials are instantiated by slow classifiers fed with costly features. We introduce Active MAP inference 1) to on-the-fly select a subset of potentials to be instantiated in the energy function, leaving the rest of the parameters of the potentials unknown, and 2) to estimate the MAP labeling from such incomplete energy function. Results for semantic segmentation benchmarks, namely PASCAL VOC 2010 and MSRC-21, show that Active MAP inference achieves similar levels of accuracy but with major efficiency gains.

This paper presents an approach to combine automatic semantic place labeling of robot-generated maps with reasoning on human route descriptions. Enabling robots to understand human route descriptions can simplify HRI situations in household or industrial settings. However, solving this problem requires handling the ambiguity present in route descriptions and the possible unreliability of the semantic perception capabilities of the robot. We address this problem by absorbing these uncertainties in a probability distribution measuring the likelihood of the different interpretations (paths) of a given route description and selecting its MAP solution. The approach is evaluated on a dataset of route descriptions transcribed into a suitable representation using standard information retrieval metrics. These performance measurements indicate that the method can correctly interpret route descriptions even in challenging environments.

Visual perception capabilities are still highly unreliable in unconstrained settings, and solutions might not be accurate in all regions of an image. Awareness of the uncertainty of perception is a fundamental requirement for proper high level decision making in a robotic system. Yet, the uncertainty measure is often sacrificed to account for dependencies between object/region classifiers. This is the case of Conditional Random Fields (CRFs), the success of which stems from their ability to infer the most likely world configuration, but they do not directly allow to estimate the uncertainty of the solution. In this paper, we consider the setting of assigning semantic labels to the pixels of an image sequence. Instead of using a CRF, we employ a Perturb-and-MAP Random Field, a recently introduced probabilistic model that allows performing fast approximate sampling from its probability density function. This allows to effectively compute the uncertainty of the solution, indicating the reliability of the most likely labeling in each region of the image. We report results on the CamVid dataset, a standard benchmark for semantic labeling of urban image sequences. In our experiments, we show the benefits of exploiting the uncertainty by putting more computational effort on the regions of the image that are less reliable, and use more efficient techniques for other regions, showing little decrease of performance.

This paper implements a real-time hand gesture recognition algorithm based on the inexpensive Kinect sensor. The use of a depth sensor allows for complex 3D gestures where the system is robust to disturbing objects or persons in the background. A Haarlet-based hand gesture recognition system is implemented to detect hand gestures in any orientation, and more in particular pointing gestures while extracting the 3D pointing direction. The system is integrated on an interactive robot (based on ROS), allowing for real-time hand gesture interaction with the robot. Pointing gestures are translated into goals for the robot, telling him where to go. A demo scenario is presented where the robot looks for persons to interact with, asks for directions, and then detects a 3D pointing direction. The robot then explores his vicinity in the given direction and looks for a new person to interact with.

Abstract — In this paper, an approach is presented that allows a robot to navigate in an urban environment by following natural language route instructions. In this situation, neither maps nor GPS information are available to the robot thus it has to rely solely on the human-given route description and the observations from its sensors. An architecture for solving problems such as navigation on the sidewalk, street direction inference, and environment labeling that arise in this situation is presented. Our initial experiments indicate that the proposed methods enable a robot to safely navigate in urban environments by following abstract route descriptions and reach previously unknown points in a city. I.

In the present paper we introduce an improved method to obtain the guaranteed singularity-free workspace of planar parallel kinematic machines. A geometric condition for the existence of singularities is extended to be used in interval analysis. Therefore, we eliminate the need of calculating the inconvenient interval form of the Jacobian's determinant. Hence, an appropriate description of the singularity-free workspace is obtained and the computational effort is reduced significantly. With the interval-based approach error sources, like manufacturing tolerances, can be considered. Consequently, regions that do not satisfy the proposed condition can be guaranteed not to have any type-two singularities. Several analysis examples demonstrate the efficiency of the proposed geometrical-based solver in comparison to existing solvers, i.e. based on the Jacobian's determinant.