3,153 research outputs found
Semantic Robot Programming for Goal-Directed Manipulation in Cluttered Scenes
We present the Semantic Robot Programming (SRP) paradigm as a convergence of
robot programming by demonstration and semantic mapping. In SRP, a user can
directly program a robot manipulator by demonstrating a snapshot of their
intended goal scene in workspace. The robot then parses this goal as a scene
graph comprised of object poses and inter-object relations, assuming known
object geometries. Task and motion planning is then used to realize the user's
goal from an arbitrary initial scene configuration. Even when faced with
different initial scene configurations, SRP enables the robot to seamlessly
adapt to reach the user's demonstrated goal. For scene perception, we propose
the Discriminatively-Informed Generative Estimation of Scenes and Transforms
(DIGEST) method to infer the initial and goal states of the world from RGBD
images. The efficacy of SRP with DIGEST perception is demonstrated for the task
of tray-setting with a Michigan Progress Fetch robot. Scene perception and task
execution are evaluated with a public household occlusion dataset and our
cluttered scene dataset.Comment: published in ICRA 201
Symbol Emergence in Robotics: A Survey
Humans can learn the use of language through physical interaction with their
environment and semiotic communication with other people. It is very important
to obtain a computational understanding of how humans can form a symbol system
and obtain semiotic skills through their autonomous mental development.
Recently, many studies have been conducted on the construction of robotic
systems and machine-learning methods that can learn the use of language through
embodied multimodal interaction with their environment and other systems.
Understanding human social interactions and developing a robot that can
smoothly communicate with human users in the long term, requires an
understanding of the dynamics of symbol systems and is crucially important. The
embodied cognition and social interaction of participants gradually change a
symbol system in a constructive manner. In this paper, we introduce a field of
research called symbol emergence in robotics (SER). SER is a constructive
approach towards an emergent symbol system. The emergent symbol system is
socially self-organized through both semiotic communications and physical
interactions with autonomous cognitive developmental agents, i.e., humans and
developmental robots. Specifically, we describe some state-of-art research
topics concerning SER, e.g., multimodal categorization, word discovery, and a
double articulation analysis, that enable a robot to obtain words and their
embodied meanings from raw sensory--motor information, including visual
information, haptic information, auditory information, and acoustic speech
signals, in a totally unsupervised manner. Finally, we suggest future
directions of research in SER.Comment: submitted to Advanced Robotic
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 State of Lifelong Learning in Service Robots: Current Bottlenecks in Object Perception and Manipulation
Service robots are appearing more and more in our daily life. The development
of service robots combines multiple fields of research, from object perception
to object manipulation. The state-of-the-art continues to improve to make a
proper coupling between object perception and manipulation. This coupling is
necessary for service robots not only to perform various tasks in a reasonable
amount of time but also to continually adapt to new environments and safely
interact with non-expert human users. Nowadays, robots are able to recognize
various objects, and quickly plan a collision-free trajectory to grasp a target
object in predefined settings. Besides, in most of the cases, there is a
reliance on large amounts of training data. Therefore, the knowledge of such
robots is fixed after the training phase, and any changes in the environment
require complicated, time-consuming, and expensive robot re-programming by
human experts. Therefore, these approaches are still too rigid for real-life
applications in unstructured environments, where a significant portion of the
environment is unknown and cannot be directly sensed or controlled. In such
environments, no matter how extensive the training data used for batch
learning, a robot will always face new objects. Therefore, apart from batch
learning, the robot should be able to continually learn about new object
categories and grasp affordances from very few training examples on-site.
Moreover, apart from robot self-learning, non-expert users could interactively
guide the process of experience acquisition by teaching new concepts, or by
correcting insufficient or erroneous concepts. In this way, the robot will
constantly learn how to help humans in everyday tasks by gaining more and more
experiences without the need for re-programming
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