1,721,043 research outputs found
Estimation of contact regions between hands and objects during human multi-digit grasping
No full textTo grasp an object successfully, we must select appropriate contact regions for our hands on the surface of the object. However, identifying such regions is challenging. This paper describes a workflow to estimate the contact regions from marker-based tracking data. Participants grasp real objects, while we track the 3D position of both the objects and the hand, including the fingers' joints. We first determine the joint Euler angles from a selection of tracked markers positioned on the back of the hand. Then, we use state-of-the-art hand mesh reconstruction algorithms to generate a mesh model of the participant's hand in the current pose and the 3D position. Using objects that were either 3D printed or 3D scanned-and are, thus, available as both real objects and mesh data-allows the hand and object meshes to be co-registered. In turn, this allows the estimation of approximate contact regions by calculating the intersections between the hand mesh and the co-registered 3D object mesh. The method may be used to estimate where and how humans grasp objects under a variety of conditions. Therefore, the method could be of interest to researchers studying visual and haptic perception, motor control, human-computer interaction in virtual and augmented reality, and robotics.</p
SESSION: Advances in the study of perception and action
No full textA large share of research in movement science is directed towards understanding the mutual interdependence of perception and action. This thematic session will outline current theoretical, methodological and modeling advances within this area.
To this end, first, the theoretical framework of internal models will be presented and its functional role in compensating for sensorimotor delays and disambiguating sensory information will be outlined. Empirical evidence for the use of minimal 3D models will be presented and the utility of these models for controlling catching behavior from a multisensory point of view will be discussed (López-Moliner, 2016).
Second, the development of a new, miniaturized eye tracking system will be presented. In combination with highly automated analysis procedures this allows to analyze “perception and action”-variables in a less restrictive and more ubiquitous way in the lab and in situ, substantiated by example studies using this technology (Kredel, 2016).
By measuring gaze and motor behavior while experimentally manipulating task constraints or goals and by using techniques developed in machine learning it is possible to gain an understanding of intrinsic decision and control processes. After an introduction to this methodology, empirically grounded implications of cost functions for visuomotor control will be presented (Rothkopf, 2016)
Continuous psychophysics differentiates visual and motor performance across motor domains
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Learning and generalizing behaviors for robots from human demonstration
No full textBehavior learning is a promising alternative to planning and control for behavior generation in robotics. The field is becoming more and more popular in applications where modeling the environment and the robot is cumbersome, difficult, or maybe even impossible.
Learning behaviors for real robots that generalize over task parameters with as few interactions with the environment as possible is a challenge that this dissertation tackles. Which problems we can currently solve with behavior learning algorithms and which algorithms we need in the domain of robotics is not apparent at the moment as there are many related fields: imitation learning, reinforcement learning, self-supervised learning, and black-box optimization.
After an extensive literature review, we decide to use methods from imitation learning and policy search to address the challenge. Specifically, we use human demonstrations recorded by motion capture systems and imitation learning with movement primitives to obtain initial behaviors that we later on generalize through contextual policy search.
Imitation from motion capture data leads to the correspondence problem: the kinematic and dynamic capabilities of humans and robots are often fundamentally different and, hence, we have to compensate for that. This thesis proposes a procedure for automatic embodiment mapping through optimization and policy search and evaluates it with several robotic systems.
Contextual policy search algorithms are often not sample efficient enough to learn directly on real robots. This thesis tries to solve the issue with active context selection, active training set selection, surrogate models, and manifold learning. The progress is illustrated with several simulated and real robot learning tasks. Strong connections between policy search and black-box optimization are revealed and exploited in this part of the thesis. This thesis demonstrates that learning manipulation behaviors is possible within a few hundred episodes directly on a real robot.
Furthermore, these new approaches to imitation learning and contextual policy search are integrated in a coherent framework that can be used
to learn new behaviors from human motion capture data almost automatically. Corresponding implementations that were developed during this thesis are available in an open source software
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
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