184,362 research outputs found
Grasp Learning: Models, Methods, and Performance
Grasp learning has become an exciting and important topic in robotics. Just a
few years ago, the problem of grasping novel objects from unstructured piles of
clutter was considered a serious research challenge. Now, it is a capability
that is quickly becoming incorporated into industrial supply chain automation.
How did that happen? What is the current state of the art in robotic grasp
learning, what are the different methodological approaches, and what machine
learning models are used? This review attempts to give an overview of the
current state of the art of grasp learning research
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The Columbia Grasp Database
Collecting grasp data for learning and benchmarking purposes is very expensive. It would be helpful to have a standard database of graspable objects, along with a set of stable grasps for each object, but no such database exists. In this work we show how to automate the construction of a database consisting of several hands, thousands of objects, and hundreds of thousands of grasps. Using this database, we demonstrate a novel grasp planning algorithm that exploits geometric similarity between a 3D model and the objects in the database to synthesize form closure grasps. Our contributions are this algorithm, and the database itself, which we are releasing to the community as a tool for both grasp planning and benchmarking
Learning to grasp in unstructured environments with deep convolutional neural networks using a Baxter Research Robot
Recent advancements in Deep Learning have accelerated the capabilities of robotic systems in terms of visual perception, object manipulation, automated navigation, and human-robot collaboration. The capability of a robotic system to manipulate objects in unstructured environments is becoming an increasingly necessary skill. Due to the dynamic nature of these environments, traditional methods, that require expert human knowledge, fail to adapt automatically. After reviewing the relevant literature a method was proposed to utilise deep transfer learning techniques to detect object grasps from coloured depth images. A grasp describes how a robotic end-effector can be arranged to securely grasp an object and successfully lift it without slippage. In this study, a ResNet-50 convolutional neural network (CNN) model is trained on the Cornell grasp dataset. The training was completed within 30 hours using a workstation PC with accelerated GPU support via an NVIDIA Titan X. The trained grasp detection model was further evaluated with a Baxter research robot and a Microsoft Kinect-v2 and a successful grasp detection accuracy of 93.91% was achieved on a diverse set of novel objects. Physical grasping trials were conducted on a set of 8 different objects. The overall system achieves an average grasp success rate of 65.0% while performing the grasp detection in under 25 milliseconds. The results analysis concluded that the objects with reasonably straight edges and moderately pronounced heights above the table are easily detected and grasped by the system
MVGrasp: Real-Time Multi-View 3D Object Grasping in Highly Cluttered Environments
Nowadays service robots are entering more and more in our daily life. In such
a dynamic environment, a robot frequently faces pile, packed, or isolated
objects. Therefore, it is necessary for the robot to know how to grasp and
manipulate various objects in different situations to help humans in everyday
tasks. Most state-of-the-art grasping approaches addressed four
degrees-of-freedom (DoF) object grasping, where the robot is forced to grasp
objects from above based on grasp synthesis of a given top-down scene. Although
such approaches showed a very good performance in predefined industrial
settings, they are not suitable for human-centric environments as the robot
will not able to grasp a range of household objects robustly, for example,
grasping a bottle from above is not stable. In this work, we propose a
multi-view deep learning approach to handle robust object grasping in
human-centric domains. In particular, our approach takes a partial point cloud
of a scene as an input, and then, generates multi-views of existing objects.
The obtained views of each object are used to estimate pixel-wise grasp
synthesis for each object. To evaluate the performance of the proposed
approach, we performed extensive experiments in both simulation and real-world
environments within the pile, packed, and isolated objects scenarios.
Experimental results showed that our approach can estimate appropriate grasp
configurations in only 22ms without the need for explicit collision checking.
Therefore, the proposed approach can be used in real-time robotic applications
that need closed-loop grasp planning
CASSL: Curriculum Accelerated Self-Supervised Learning
Recent self-supervised learning approaches focus on using a few thousand data
points to learn policies for high-level, low-dimensional action spaces.
However, scaling this framework for high-dimensional control require either
scaling up the data collection efforts or using a clever sampling strategy for
training. We present a novel approach - Curriculum Accelerated Self-Supervised
Learning (CASSL) - to train policies that map visual information to high-level,
higher- dimensional action spaces. CASSL orders the sampling of training data
based on control dimensions: the learning and sampling are focused on few
control parameters before other parameters. The right curriculum for learning
is suggested by variance-based global sensitivity analysis of the control
space. We apply our CASSL framework to learning how to grasp using an adaptive,
underactuated multi-fingered gripper, a challenging system to control. Our
experimental results indicate that CASSL provides significant improvement and
generalization compared to baseline methods such as staged curriculum learning
(8% increase) and complete end-to-end learning with random exploration (14%
improvement) tested on a set of novel objects
Simultaneous Multi-View Object Recognition and Grasping in Open-Ended Domains
A robot working in human-centric environments needs to know which kind of
objects exist in the scene, where they are, and how to grasp and manipulate
various objects in different situations to help humans in everyday tasks.
Therefore, object recognition and grasping are two key functionalities for such
robots. Most state-of-the-art tackles object recognition and grasping as two
separate problems while both use visual input. Furthermore, the knowledge of
the robot is fixed after the training phase. In such cases, if the robot faces
new object categories, it must retrain from scratch to incorporate new
information without catastrophic interference. To address this problem, we
propose a deep learning architecture with augmented memory capacities to handle
open-ended object recognition and grasping simultaneously. In particular, our
approach takes multi-views of an object as input and jointly estimates
pixel-wise grasp configuration as well as a deep scale- and rotation-invariant
representation as outputs. The obtained representation is then used for
open-ended object recognition through a meta-active learning technique. We
demonstrate the ability of our approach to grasp never-seen-before objects and
to rapidly learn new object categories using very few examples on-site in both
simulation and real-world settings.Comment: arXiv admin note: text overlap with arXiv:2103.1099
Notions of optimal transport theory and how to implement them on a computer
This article gives an introduction to optimal transport, a mathematical
theory that makes it possible to measure distances between functions (or
distances between more general objects), to interpolate between objects or to
enforce mass/volume conservation in certain computational physics simulations.
Optimal transport is a rich scientific domain, with active research
communities, both on its theoretical aspects and on more applicative
considerations, such as geometry processing and machine learning. This article
aims at explaining the main principles behind the theory of optimal transport,
introduce the different involved notions, and more importantly, how they
relate, to let the reader grasp an intuition of the elegant theory that
structures them. Then we will consider a specific setting, called
semi-discrete, where a continuous function is transported to a discrete sum of
Dirac masses. Studying this specific setting naturally leads to an efficient
computational algorithm, that uses classical notions of computational geometry,
such as a generalization of Voronoi diagrams called Laguerre diagrams.Comment: 32 pages, 17 figure
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