293 research outputs found
Open Arms: Open-Source Arms, Hands & Control
Open Arms is a novel open-source platform of realistic human-like robotic
hands and arms hardware with 28 Degree-of-Freedom (DoF), designed to extend the
capabilities and accessibility of humanoid robotic grasping and manipulation.
The Open Arms framework includes an open SDK and development environment,
simulation tools, and application development tools to build and operate Open
Arms. This paper describes these hands controls, sensing, mechanisms, aesthetic
design, and manufacturing and their real-world applications with a teleoperated
nursing robot. From 2015 to 2022, the authors have designed and established the
manufacturing of Open Arms as a low-cost, high functionality robotic arms
hardware and software framework to serve both humanoid robot applications and
the urgent demand for low-cost prosthetics, as part of the Hanson Robotics
Sophia Robot platform. Using the techniques of consumer product manufacturing,
we set out to define modular, low-cost techniques for approximating the
dexterity and sensitivity of human hands. To demonstrate the dexterity and
control of our hands, we present a Generative Grasping Residual CNN (GGR-CNN)
model that can generate robust antipodal grasps from input images of various
objects in real-time speeds (22ms). We achieved state-of-the-art accuracy of
92.4% using our model architecture on a standard Cornell Grasping Dataset,
which contains a diverse set of household objects.Comment: Submitted to 36th Conference on Neural Information Processing Systems
(NeurIPS 2022
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
Jacquard: A Large Scale Dataset for Robotic Grasp Detection
Grasping skill is a major ability that a wide number of real-life
applications require for robotisation. State-of-the-art robotic grasping
methods perform prediction of object grasp locations based on deep neural
networks. However, such networks require huge amount of labeled data for
training making this approach often impracticable in robotics. In this paper,
we propose a method to generate a large scale synthetic dataset with ground
truth, which we refer to as the Jacquard grasping dataset. Jacquard is built on
a subset of ShapeNet, a large CAD models dataset, and contains both RGB-D
images and annotations of successful grasping positions based on grasp attempts
performed in a simulated environment. We carried out experiments using an
off-the-shelf CNN, with three different evaluation metrics, including real
grasping robot trials. The results show that Jacquard enables much better
generalization skills than a human labeled dataset thanks to its diversity of
objects and grasping positions. For the purpose of reproducible research in
robotics, we are releasing along with the Jacquard dataset a web interface for
researchers to evaluate the successfulness of their grasping position
detections using our dataset
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