5,122 research outputs found
Active vision for dexterous grasping of novel objects
How should a robot direct active vision so as to ensure reliable grasping? We
answer this question for the case of dexterous grasping of unfamiliar objects.
By dexterous grasping we simply mean grasping by any hand with more than two
fingers, such that the robot has some choice about where to place each finger.
Such grasps typically fail in one of two ways, either unmodeled objects in the
scene cause collisions or object reconstruction is insufficient to ensure that
the grasp points provide a stable force closure. These problems can be solved
more easily if active sensing is guided by the anticipated actions. Our
approach has three stages. First, we take a single view and generate candidate
grasps from the resulting partial object reconstruction. Second, we drive the
active vision approach to maximise surface reconstruction quality around the
planned contact points. During this phase, the anticipated grasp is continually
refined. Third, we direct gaze to improve the safety of the planned reach to
grasp trajectory. We show, on a dexterous manipulator with a camera on the
wrist, that our approach (80.4% success rate) outperforms a randomised
algorithm (64.3% success rate).Comment: IROS 2016. Supplementary video: https://youtu.be/uBSOO6tMzw
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
Multimodal Bayesian Network for Artificial Perception
In order to make machines perceive their external environment coherently, multiple sources of sensory information derived from several different modalities can be used (e.g. cameras, LIDAR, stereo, RGB-D, and radars). All these different sources of information can be efficiently merged to form a robust perception of the environment. Some of the mechanisms that underlie this merging of the sensor information are highlighted in this chapter, showing that depending on the type of information, different combination and integration strategies can be used and that prior knowledge are often required for interpreting the sensory signals efficiently. The notion that perception involves Bayesian inference is an increasingly popular position taken by a considerable number of researchers. Bayesian models have provided insights into many perceptual phenomena, showing that they are a valid approach to deal with real-world uncertainties and for robust classification, including classification in time-dependent problems. This chapter addresses the use of Bayesian networks applied to sensory perception in the following areas: mobile robotics, autonomous driving systems, advanced driver assistance systems, sensor fusion for object detection, and EEG-based mental states classification
Data-Driven Shape Analysis and Processing
Data-driven methods play an increasingly important role in discovering
geometric, structural, and semantic relationships between 3D shapes in
collections, and applying this analysis to support intelligent modeling,
editing, and visualization of geometric data. In contrast to traditional
approaches, a key feature of data-driven approaches is that they aggregate
information from a collection of shapes to improve the analysis and processing
of individual shapes. In addition, they are able to learn models that reason
about properties and relationships of shapes without relying on hard-coded
rules or explicitly programmed instructions. We provide an overview of the main
concepts and components of these techniques, and discuss their application to
shape classification, segmentation, matching, reconstruction, modeling and
exploration, as well as scene analysis and synthesis, through reviewing the
literature and relating the existing works with both qualitative and numerical
comparisons. We conclude our report with ideas that can inspire future research
in data-driven shape analysis and processing.Comment: 10 pages, 19 figure
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