156 research outputs found
A novel integrated method of detection-grasping for specific object based on the box coordinate matching
To better care for the elderly and disabled, it is essential for service
robots to have an effective fusion method of object detection and grasp
estimation. However, limited research has been observed on the combination of
object detection and grasp estimation. To overcome this technical difficulty, a
novel integrated method of detection-grasping for specific object based on the
box coordinate matching is proposed in this paper. Firstly, the SOLOv2 instance
segmentation model is improved by adding channel attention module (CAM) and
spatial attention module (SAM). Then, the atrous spatial pyramid pooling (ASPP)
and CAM are added to the generative residual convolutional neural network
(GR-CNN) model to optimize grasp estimation. Furthermore, a detection-grasping
integrated algorithm based on box coordinate matching (DG-BCM) is proposed to
obtain the fusion model of object detection and grasp estimation. For
verification, experiments on object detection and grasp estimation are
conducted separately to verify the superiority of improved models.
Additionally, grasping tasks for several specific objects are implemented on a
simulation platform, demonstrating the feasibility and effectiveness of DG-BCM
algorithm proposed in this paper
Efficient Fully Convolution Neural Network for Generating Pixel Wise Robotic Grasps With High Resolution Images
This paper presents an efficient neural network model to generate robotic
grasps with high resolution images. The proposed model uses fully convolution
neural network to generate robotic grasps for each pixel using 400 400
high resolution RGB-D images. It first down-sample the images to get features
and then up-sample those features to the original size of the input as well as
combines local and global features from different feature maps. Compared to
other regression or classification methods for detecting robotic grasps, our
method looks more like the segmentation methods which solves the problem
through pixel-wise ways. We use Cornell Grasp Dataset to train and evaluate the
model and get high accuracy about 94.42% for image-wise and 91.02% for
object-wise and fast prediction time about 8ms. We also demonstrate that
without training on the multiple objects dataset, our model can directly output
robotic grasps candidates for different objects because of the pixel wise
implementation.Comment: Submitted to ROBIO 201
A Robotic Visual Grasping Design: Rethinking Convolution Neural Network with High-Resolutions
High-resolution representations are important for vision-based robotic
grasping problems. Existing works generally encode the input images into
low-resolution representations via sub-networks and then recover
high-resolution representations. This will lose spatial information, and errors
introduced by the decoder will be more serious when multiple types of objects
are considered or objects are far away from the camera. To address these
issues, we revisit the design paradigm of CNN for robotic perception tasks. We
demonstrate that using parallel branches as opposed to serial stacked
convolutional layers will be a more powerful design for robotic visual grasping
tasks. In particular, guidelines of neural network design are provided for
robotic perception tasks, e.g., high-resolution representation and lightweight
design, which respond to the challenges in different manipulation scenarios. We
then develop a novel grasping visual architecture referred to as HRG-Net, a
parallel-branch structure that always maintains a high-resolution
representation and repeatedly exchanges information across resolutions.
Extensive experiments validate that these two designs can effectively enhance
the accuracy of visual-based grasping and accelerate network training. We show
a series of comparative experiments in real physical environments at Youtube:
https://youtu.be/Jhlsp-xzHFY
Instance-wise Grasp Synthesis for Robotic Grasping
Generating high-quality instance-wise grasp configurations provides critical
information of how to grasp specific objects in a multi-object environment and
is of high importance for robot manipulation tasks. This work proposed a novel
\textbf{S}ingle-\textbf{S}tage \textbf{G}rasp (SSG) synthesis network, which
performs high-quality instance-wise grasp synthesis in a single stage: instance
mask and grasp configurations are generated for each object simultaneously. Our
method outperforms state-of-the-art on robotic grasp prediction based on the
OCID-Grasp dataset, and performs competitively on the JACQUARD dataset. The
benchmarking results showed significant improvements compared to the baseline
on the accuracy of generated grasp configurations. The performance of the
proposed method has been validated through both extensive simulations and real
robot experiments for three tasks including single object pick-and-place, grasp
synthesis in cluttered environments and table cleaning task
Modular Anti-noise Deep Learning Network for Robotic Grasp Detection Based on RGB Images
While traditional methods relies on depth sensors, the current trend leans
towards utilizing cost-effective RGB images, despite their absence of depth
cues. This paper introduces an interesting approach to detect grasping pose
from a single RGB image. To this end, we propose a modular learning network
augmented with grasp detection and semantic segmentation, tailored for robots
equipped with parallel-plate grippers. Our network not only identifies
graspable objects but also fuses prior grasp analyses with semantic
segmentation, thereby boosting grasp detection precision. Significantly, our
design exhibits resilience, adeptly handling blurred and noisy visuals. Key
contributions encompass a trainable network for grasp detection from RGB
images, a modular design facilitating feasible grasp implementation, and an
architecture robust against common image distortions. We demonstrate the
feasibility and accuracy of our proposed approach through practical experiments
and evaluations
Sim2Real Grasp Pose Estimation for Adaptive Robotic Applications
Adaptive robotics plays an essential role in achieving truly co-creative
cyber physical systems. In robotic manipulation tasks, one of the biggest
challenges is to estimate the pose of given workpieces. Even though the recent
deep-learning-based models show promising results, they require an immense
dataset for training. In this paper, we propose two vision-based, multiobject
grasp-pose estimation models, the MOGPE Real-Time (RT) and the MOGPE
High-Precision (HP). Furthermore, a sim2real method based on domain
randomization to diminish the reality gap and overcome the data shortage. We
yielded an 80% and a 96.67% success rate in a real-world robotic pick-and-place
experiment, with the MOGPE RT and the MOGPE HP model respectively. Our
framework provides an industrial tool for fast data generation and model
training and requires minimal domain-specific data.Comment: Submitted to the 22nd World Congress of the International Federation
of Automatic Control (IFAC 2023
Scene Understanding for Autonomous Manipulation with Deep Learning
Over the past few years, deep learning techniques have achieved tremendous success
in many visual understanding tasks such as object detection, image segmentation,
and caption generation. Despite this thriving in computer vision and natural language
processing, deep learning has not yet shown signicant impact in robotics.
Due to the gap between theory and application, there are many challenges when
applying the results of deep learning to the real robotic systems. In this study,
our long-term goal is to bridge the gap between computer vision and robotics by
developing visual methods that can be used in real robots. In particular, this work
tackles two fundamental visual problems for autonomous robotic manipulation: affordance
detection and ne-grained action understanding. Theoretically, we propose
dierent deep architectures to further improves the state of the art in each problem.
Empirically, we show that the outcomes of our proposed methods can be applied in
real robots and allow them to perform useful manipulation tasks
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