Top-down and bottom-up aspects of active search in a real-world environment

Visual search has been studied intensively in the labouratory, but lab search often differs from search in the real world in many respects. Here, we used a mobile eye tracker to record the gaze of participants engaged in a realistic, active search task. Participants were asked to walk into a mailroom and locate a target mailbox among many similar mailboxes. This procedure allowed control of bottom-up cues (by making the target mailbox more salient; Experiment 1) and top-down instructions (by informing participants about the cue; Experiment 2). The bottom-up salience of the target had no effect on the overall time taken to search for the target, although the salient target was more likely to be fixated and found once it was within the central visual field. Top-down knowledge of target appearance had a larger effect, reducing the need for multiple head and body movements, and meaning that the target was fixated earlier and from further away. Although there remains much to be discovered in complex real-world search, this study demonstrates that principles from visual search in the labouratory influence gaze in natural behaviour, and provides a bridge between these labouratory studies and research examining vision in natural tasks. (PsycINFO Database Record © 2014 APA, all rights reserved)

Conditions for suboptimal filter stability in SLAM

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2004, Sendai (Japón)In this article, we show marginal stability in SLAM, guaranteeing convergence to a non-zero mean state error estimate bounded by a constant value. Moreover, marginal stability guarantees also convergence of the Riccati equation of the one-step ahead state error covariance to at least one psd steady state solution. In the search for real time implementations of SLAM, covariance inflation methods produce a suboptimal filter that eventually may lead to the computation of an unbounded state error covariance. We provide tight constraints in the amount of decorrelation possible, to guarantee convergence of the state error covariance, and at the same time, a linear-time implementation of SLAM.This work was supported by the project 'Supervised learning of industrial scenes by means of an active vision equipped mobile robot.' (J-00063).Peer Reviewe

Learning To Scale Up Search-Driven Data Integration

A recent movement to tackle the long-standing data integration problem is a compositional and iterative approach, termed “pay-as-you-go” data integration. Under this model, the objective is to immediately support queries over “partly integrated” data, and to enable the user community to drive integration of the data that relate to their actual information needs. Over time, data will be gradually integrated. While the pay-as-you-go vision has been well-articulated for some time, only recently have we begun to understand how it can be manifested into a system implementation. One branch of this effort has focused on enabling queries through keyword search-driven data integration, in which users pose queries over partly integrated data encoded as a graph, receive ranked answers generated from data and metadata that is linked at query-time, and provide feedback on those answers. From this user feedback, the system learns to repair bad schema matches or record links. Many real world issues of uncertainty and diversity in search-driven integration remain open. Such tasks in search-driven integration require a combination of human guidance and machine learning. The challenge is how to make maximal use of limited human input. This thesis develops three methods to scale up search-driven integration, through learning from expert feedback: (1) active learning techniques to repair links from small amounts of user feedback; (2) collaborative learning techniques to combine users’ conflicting feedback; and (3) debugging techniques to identify where data experts could best improve integration quality. We implement these methods within the Q System, a prototype of search-driven integration, and validate their effectiveness over real-world datasets

Arc welding quality monitoring by means of near infrared imaging spectroscopy

The search for an efficient on-line monitoring system focused on the real-time analysis of the welding quality is an active area of research, mainly due to the widespread use of both arc and laser welding processes in relevant industrial scenarios such as aeronautics or nuclear. In this work, an improvement in the performance of a previously designed monitor system is presented. This improvement is accomplished by the employment of a dual spatial-spectral technique, namely imaging spectroscopy. This technique allows the simultaneous determination of the optical spectrum components and the spatial location of an object in a surface. In this way, the spatially characterization of the plasma emitted during a tungsten inert gas (TIG) welding is performed. The main advantage of this technique is that the spectra of all the points in the line of vision are measured at the same time. Not only are all the spectra captured simultaneously, but they are also processed as a batch, allowing the investigation of the welding quality. Moreover, imaging spectroscopy provides the desired real-time operation. To simultaneously acquire the information of both domains, spectral and spatial, a passive Prism-Grating-Prism (PGP) device can be used. In this paper the plasma spectra is captured during the welding test by means of a near infrared imaging spectroscopic system which consists of input optics, an imaging spectrograph and a monochrome camera. Technique features regarding on-line welding quality monitoring are discussed by means of several experimental welding tests

Active End-Effector Pose Selection for Tactile Object Recognition through Monte Carlo Tree Search

This paper considers the problem of active object recognition using touch only. The focus is on adaptively selecting a sequence of wrist poses that achieves accurate recognition by enclosure grasps. It seeks to minimize the number of touches and maximize recognition confidence. The actions are formulated as wrist poses relative to each other, making the algorithm independent of absolute workspace coordinates. The optimal sequence is approximated by Monte Carlo tree search. We demonstrate results in a physics engine and on a real robot. In the physics engine, most object instances were recognized in at most 16 grasps. On a real robot, our method recognized objects in 2--9 grasps and outperformed a greedy baseline.Comment: Accepted to International Conference on Intelligent Robots and Systems (IROS) 201

Active End-Effector Pose Selection for Tactile Object Recognition through Monte Carlo Tree Search

This paper considers the problem of active object recognition using touch only. The focus is on adaptively selecting a sequence of wrist poses that achieves accurate recognition by enclosure grasps. It seeks to minimize the number of touches and maximize recognition confidence. The actions are formulated as wrist poses relative to each other, making the algorithm independent of absolute workspace coordinates. The optimal sequence is approximated by Monte Carlo tree search. We demonstrate results in a physics engine and on a real robot. In the physics engine, most object instances were recognized in at most 16 grasps. On a real robot, our method recognized objects in 2--9 grasps and outperformed a greedy baseline.Comment: Accepted to International Conference on Intelligent Robots and Systems (IROS) 201