615 research outputs found
Learning grasp affordance reasoning through semantic relations
Reasoning about object affordances allows an autonomous agent to perform
generalised manipulation tasks among object instances. While current approaches
to grasp affordance estimation are effective, they are limited to a single
hypothesis. We present an approach for detection and extraction of multiple
grasp affordances on an object via visual input. We define semantics as a
combination of multiple attributes, which yields benefits in terms of
generalisation for grasp affordance prediction. We use Markov Logic Networks to
build a knowledge base graph representation to obtain a probability
distribution of grasp affordances for an object. To harvest the knowledge base,
we collect and make available a novel dataset that relates different semantic
attributes. We achieve reliable mappings of the predicted grasp affordances on
the object by learning prototypical grasping patches from several examples. We
show our method's generalisation capabilities on grasp affordance prediction
for novel instances and compare with similar methods in the literature.
Moreover, using a robotic platform, on simulated and real scenarios, we
evaluate the success of the grasping task when conditioned on the grasp
affordance prediction.Comment: Accepted in IEEE/RSJ International Conference on Intelligent Robots
and Systems (IROS) 201
Affordances in Psychology, Neuroscience, and Robotics: A Survey
The concept of affordances appeared in psychology during the late 60s as an alternative perspective on the visual perception of the environment. It was revolutionary in the intuition that the way living beings perceive the world is deeply influenced by the actions they are able to perform. Then, across the last 40 years, it has influenced many applied fields, e.g., design, human-computer interaction, computer vision, and robotics. In this paper, we offer a multidisciplinary perspective on the notion of affordances. We first discuss the main definitions and formalizations of the affordance theory, then we report the most significant evidence in psychology and neuroscience that support it, and finally we review the most relevant applications of this concept in robotics
Multi-Object Graph Affordance Network: Enabling Goal-Oriented Planning through Compound Object Affordances
Learning object affordances is an effective tool in the field of robot
learning. While the data-driven models delve into the exploration of
affordances of single or paired objects, there is a notable gap in the
investigation of affordances of compound objects that are composed of an
arbitrary number of objects with complex shapes. In this study, we propose
Multi-Object Graph Affordance Network (MOGAN) that models compound object
affordances and predicts the effect of placing new objects on top of the
existing compound. Given different tasks, such as building towers of specific
heights or properties, we used a search based planning to find the sequence of
stack actions with the objects of suitable affordances. We showed that our
system was able to correctly model the affordances of very complex compound
objects that include stacked spheres and cups, poles, and rings that enclose
the poles. We demonstrated the applicability of our system in both simulated
and real-world environments, comparing our systems with a baseline model to
highlight its advantages
Detecting Object Affordances with Convolutional Neural Networks
We present a novel and real-time method to detect
object affordances from RGB-D images. Our method trains
a deep Convolutional Neural Network (CNN) to learn deep
features from the input data in an end-to-end manner. The CNN
has an encoder-decoder architecture in order to obtain smooth
label predictions. The input data are represented as multiple
modalities to let the network learn the features more effectively.
Our method sets a new benchmark on detecting object affordances, improving the accuracy by 20% in comparison with
the state-of-the-art methods that use hand-designed geometric
features. Furthermore, we apply our detection method on a
full-size humanoid robot (WALK-MAN) to demonstrate that
the robot is able to perform grasps after efficiently detecting
the object affordances
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
Bootstrapping Relational Affordances of Object Pairs using Transfer
This work was supported in part by the U.K. EPSRC DTG EP/J5000343/1 at Aberdeen, and in part by the EU Cognitive Systems Project XPERIENCE at SDU under Grant FP7-ICT-270273.Peer reviewedPostprin
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