35,113 research outputs found
Data-Driven Grasp Synthesis - A Survey
We review the work on data-driven grasp synthesis and the methodologies for
sampling and ranking candidate grasps. We divide the approaches into three
groups based on whether they synthesize grasps for known, familiar or unknown
objects. This structure allows us to identify common object representations and
perceptual processes that facilitate the employed data-driven grasp synthesis
technique. In the case of known objects, we concentrate on the approaches that
are based on object recognition and pose estimation. In the case of familiar
objects, the techniques use some form of a similarity matching to a set of
previously encountered objects. Finally for the approaches dealing with unknown
objects, the core part is the extraction of specific features that are
indicative of good grasps. Our survey provides an overview of the different
methodologies and discusses open problems in the area of robot grasping. We
also draw a parallel to the classical approaches that rely on analytic
formulations.Comment: 20 pages, 30 Figures, submitted to IEEE Transactions on Robotic
Multi-Modal Human-Machine Communication for Instructing Robot Grasping Tasks
A major challenge for the realization of intelligent robots is to supply them
with cognitive abilities in order to allow ordinary users to program them
easily and intuitively. One way of such programming is teaching work tasks by
interactive demonstration. To make this effective and convenient for the user,
the machine must be capable to establish a common focus of attention and be
able to use and integrate spoken instructions, visual perceptions, and
non-verbal clues like gestural commands. We report progress in building a
hybrid architecture that combines statistical methods, neural networks, and
finite state machines into an integrated system for instructing grasping tasks
by man-machine interaction. The system combines the GRAVIS-robot for visual
attention and gestural instruction with an intelligent interface for speech
recognition and linguistic interpretation, and an modality fusion module to
allow multi-modal task-oriented man-machine communication with respect to
dextrous robot manipulation of objects.Comment: 7 pages, 8 figure
Natural Virtual Reality User Interface to Define Assembly Sequences for Digital Human Models
Digital human models (DHMs) are virtual representations of human beings. They are used to conduct, among other things, ergonomic assessments in factory layout planning. DHM software tools are challenging in their use and thus require a high amount of training for engineers. In this paper, we present a virtual reality (VR) application that enables engineers to work with DHMs easily. Since VR systems with head-mounted displays (HMDs) are less expensive than CAVE systems, HMDs can be integrated more extensively into the product development process. Our application provides a reality-based interface and allows users to conduct an assembly task in VR and thus to manipulate the virtual scene with their real hands. These manipulations are used as input for the DHM to simulate, on that basis, human ergonomics. Therefore, we introduce a software and hardware architecture, the VATS (virtual action tracking system). This paper furthermore presents the results of a user study in which the VATS was compared to the existing WIMP (Windows, Icons, Menus and Pointer) interface. The results show that the VATS system enables users to conduct tasks in a significantly faster way
Online weight estimation in a robotic gripper arm
This paper presents a novel methodology for online, fast and accurate weight estimation technique in a robotic gripper arm. The laboratory setup is inspired from several real life applications of weight estimation in moving cranes, e.g. loading containers in a shipyard, iron scrapping in steel industry, etc. The weight needs to be estimated within a specified time interval and within a tolerance interval for accuracy. The results indicate that the proposed method is suitable for this kind of application and an improvement of 30% has been achieved compared to the current state of work
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