3,764 research outputs found
Densely Supervised Grasp Detector (DSGD)
This paper presents Densely Supervised Grasp Detector (DSGD), a deep learning
framework which combines CNN structures with layer-wise feature fusion and
produces grasps and their confidence scores at different levels of the image
hierarchy (i.e., global-, region-, and pixel-levels). % Specifically, at the
global-level, DSGD uses the entire image information to predict a grasp. At the
region-level, DSGD uses a region proposal network to identify salient regions
in the image and predicts a grasp for each salient region. At the pixel-level,
DSGD uses a fully convolutional network and predicts a grasp and its confidence
at every pixel. % During inference, DSGD selects the most confident grasp as
the output. This selection from hierarchically generated grasp candidates
overcomes limitations of the individual models. % DSGD outperforms
state-of-the-art methods on the Cornell grasp dataset in terms of grasp
accuracy. % Evaluation on a multi-object dataset and real-world robotic
grasping experiments show that DSGD produces highly stable grasps on a set of
unseen objects in new environments. It achieves 97% grasp detection accuracy
and 90% robotic grasping success rate with real-time inference speed
DUQIM-Net: Probabilistic Object Hierarchy Representation for Multi-View Manipulation
Object manipulation in cluttered scenes is a difficult and important problem
in robotics. To efficiently manipulate objects, it is crucial to understand
their surroundings, especially in cases where multiple objects are stacked one
on top of the other, preventing effective grasping. We here present DUQIM-Net,
a decision-making approach for object manipulation in a setting of stacked
objects. In DUQIM-Net, the hierarchical stacking relationship is assessed using
Adj-Net, a model that leverages existing Transformer Encoder-Decoder object
detectors by adding an adjacency head. The output of this head
probabilistically infers the underlying hierarchical structure of the objects
in the scene. We utilize the properties of the adjacency matrix in DUQIM-Net to
perform decision making and assist with object-grasping tasks. Our experimental
results show that Adj-Net surpasses the state-of-the-art in object-relationship
inference on the Visual Manipulation Relationship Dataset (VMRD), and that
DUQIM-Net outperforms comparable approaches in bin clearing tasks.Comment: 8 pages, 6 figures, 3 tables. Accepted to the 2022 IEEE/RSJ
International Conference on Intelligent Robots and Systems (IROS 2022
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