ToF cameras for active vision in robotics

ToF cameras are now a mature technology that is widely being adopted to provide sensory input to robotic applications. Depending on the nature of the objects to be perceived and the viewing distance, we distinguish two groups of applications: those requiring to capture the whole scene and those centered on an object. It will be demonstrated that it is in this last group of applications, in which the robot has to locate and possibly manipulate an object, where the distinctive characteristics of ToF cameras can be better exploited. After presenting the physical sensor features and the calibration requirements of such cameras, we review some representative works highlighting for each one which of the distinctive ToF characteristics have been more essential. Even if at low resolution, the acquisition of 3D images at frame-rate is one of the most important features, as it enables quick background/ foreground segmentation. A common use is in combination with classical color cameras. We present three developed applications, using a mobile robot and a robotic arm, to exemplify with real images some of the stated advantages.This work was supported by the EU project GARNICS FP7-247947, by the Spanish Ministry of Science and Innovation under project PAU+ DPI2011-27510, and by the Catalan Research Commission through SGR-00155Peer Reviewe

多層マルチモーダルLDAを用いた複数概念の統合に関する研究

知能ロボット開発において，ロボットが物体を扱うために，物体のカテゴリ分類だけではなく，物体と動きやその使い方など，物体概念と他の概念との関係を獲得する必要があると言える．さらに，ロボットによる真の理解を実現するために，場所や人物といった物事に対する概念の獲得も必要とする．本研究では，多層マルチモーダルLDA(mMLDA)に基づく，ロボットによる多様な概念形成及び統合を実現する．mMLDAによって，概念の形成と統合を同時に獲得が可能であるため，各概念の形成が互いに影響しあって，より正しく形成できる．さらに，我々が用いている言語もカテゴリに基づいており，ロボットもカテゴリ分類を通じて物体の概念を学習することで，未観測情報の予測や言語の理解が可能になると考えられる．言語理解のためのロボットによる語意の獲得問題についても，mMLDAを用いて実現することが可能である．本研究では，単語と概念間の相互情報量を用いることで，どの単語が本来どの概念に結びついているのかを自動的に推定する手法を提案する．また，単語と概念の結び付きを用いて，教示発話における概念の発火順を学習することが可能であり，これを学習することで，観測した情報を表現する文章を生成することができる．提案したこれらのモデルを実験によって，その有効性を示した．電気通信大学201

RoboCup@Home: Analysis and results of evolving competitions for domestic and service robots

Scientific competitions are becoming more common in many research areas of artificial intelligence and robotics, since they provide a shared testbed for comparing different solutions and enable the exchange of research results. Moreover, they are interesting for general audiences and industries. Currently, many major research areas in artificial intelligence and robotics are organizing multiple-year competitions that are typically associated with scientific conferences. One important aspect of such competitions is that they are organized for many years. This introduces a temporal evolution that is interesting to analyze. However, the problem of evaluating a competition over many years remains unaddressed. We believe that this issue is critical to properly fuel changes over the years and measure the results of these decisions. Therefore, this article focuses on the analysis and the results of evolving competitions. In this article, we present the RoboCup@Home competition, which is the largest worldwide competition for domestic service robots, and evaluate its progress over the past seven years. We show how the definition of a proper scoring system allows for desired functionalities to be related to tasks and how the resulting analysis fuels subsequent changes to achieve general and robust solutions implemented by the teams. Our results show not only the steadily increasing complexity of the tasks that RoboCup@Home robots can solve but also the increased performance for all of the functionalities addressed in the competition. We believe that the methodology used in RoboCup@Home for evaluating competition advances and for stimulating changes can be applied and extended to other robotic competitions as well as to multi-year research projects involving Artificial Intelligence and Robotics