2,927 research outputs found
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
The NASA/OAST telerobot testbed architecture
Through a phased development such as a laboratory-based research testbed, the NASA/OAST Telerobot Testbed provides an environment for system test and demonstration of the technology which will usefully complement, significantly enhance, or even replace manned space activities. By integrating advanced sensing, robotic manipulation and intelligent control under human-interactive supervision, the Testbed will ultimately demonstrate execution of a variety of generic tasks suggestive of space assembly, maintenance, repair, and telescience. The Testbed system features a hierarchical layered control structure compatible with the incorporation of evolving technologies as they become available. The Testbed system is physically implemented in a computing architecture which allows for ease of integration of these technologies while preserving the flexibility for test of a variety of man-machine modes. The development currently in progress on the functional and implementation architectures of the NASA/OAST Testbed and capabilities planned for the coming years are presented
A Continuous Grasp Representation for the Imitation Learning of Grasps on Humanoid Robots
Models and methods are presented which enable a humanoid robot to learn reusable, adaptive grasping skills. Mechanisms and principles in human grasp behavior are studied. The findings are used to develop a grasp representation capable of retaining specific motion characteristics and of adapting to different objects and tasks. Based on the representation a framework is proposed which enables the robot to observe human grasping, learn grasp representations, and infer executable grasping actions
A Survey of Knowledge Representation in Service Robotics
Within the realm of service robotics, researchers have placed a great amount
of effort into learning, understanding, and representing motions as
manipulations for task execution by robots. The task of robot learning and
problem-solving is very broad, as it integrates a variety of tasks such as
object detection, activity recognition, task/motion planning, localization,
knowledge representation and retrieval, and the intertwining of
perception/vision and machine learning techniques. In this paper, we solely
focus on knowledge representations and notably how knowledge is typically
gathered, represented, and reproduced to solve problems as done by researchers
in the past decades. In accordance with the definition of knowledge
representations, we discuss the key distinction between such representations
and useful learning models that have extensively been introduced and studied in
recent years, such as machine learning, deep learning, probabilistic modelling,
and semantic graphical structures. Along with an overview of such tools, we
discuss the problems which have existed in robot learning and how they have
been built and used as solutions, technologies or developments (if any) which
have contributed to solving them. Finally, we discuss key principles that
should be considered when designing an effective knowledge representation.Comment: Accepted for RAS Special Issue on Semantic Policy and Action
Representations for Autonomous Robots - 22 Page
Learning Generalizable Dexterous Manipulation from Human Grasp Affordance
Dexterous manipulation with a multi-finger hand is one of the most
challenging problems in robotics. While recent progress in imitation learning
has largely improved the sample efficiency compared to Reinforcement Learning,
the learned policy can hardly generalize to manipulate novel objects, given
limited expert demonstrations. In this paper, we propose to learn dexterous
manipulation using large-scale demonstrations with diverse 3D objects in a
category, which are generated from a human grasp affordance model. This
generalizes the policy to novel object instances within the same category. To
train the policy, we propose a novel imitation learning objective jointly with
a geometric representation learning objective using our demonstrations. By
experimenting with relocating diverse objects in simulation, we show that our
approach outperforms baselines with a large margin when manipulating novel
objects. We also ablate the importance on 3D object representation learning for
manipulation. We include videos, code, and additional information on the
project website - https://kristery.github.io/ILAD/ .Comment: project page: https://kristery.github.io/ILAD
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