11,079 research outputs found
Learning to Place New Objects
The ability to place objects in the environment is an important skill for a
personal robot. An object should not only be placed stably, but should also be
placed in its preferred location/orientation. For instance, a plate is
preferred to be inserted vertically into the slot of a dish-rack as compared to
be placed horizontally in it. Unstructured environments such as homes have a
large variety of object types as well as of placing areas. Therefore our
algorithms should be able to handle placing new object types and new placing
areas. These reasons make placing a challenging manipulation task. In this
work, we propose a supervised learning algorithm for finding good placements
given the point-clouds of the object and the placing area. It learns to combine
the features that capture support, stability and preferred placements using a
shared sparsity structure in the parameters. Even when neither the object nor
the placing area is seen previously in the training set, our algorithm predicts
good placements. In extensive experiments, our method enables the robot to
stably place several new objects in several new placing areas with 98%
success-rate; and it placed the objects in their preferred placements in 92% of
the cases
Fine-grained Search Space Classification for Hard Enumeration Variants of Subset Problems
We propose a simple, powerful, and flexible machine learning framework for
(i) reducing the search space of computationally difficult enumeration variants
of subset problems and (ii) augmenting existing state-of-the-art solvers with
informative cues arising from the input distribution. We instantiate our
framework for the problem of listing all maximum cliques in a graph, a central
problem in network analysis, data mining, and computational biology. We
demonstrate the practicality of our approach on real-world networks with
millions of vertices and edges by not only retaining all optimal solutions, but
also aggressively pruning the input instance size resulting in several fold
speedups of state-of-the-art algorithms. Finally, we explore the limits of
scalability and robustness of our proposed framework, suggesting that
supervised learning is viable for tackling NP-hard problems in practice.Comment: AAAI 201
Representation Independent Analytics Over Structured Data
Database analytics algorithms leverage quantifiable structural properties of
the data to predict interesting concepts and relationships. The same
information, however, can be represented using many different structures and
the structural properties observed over particular representations do not
necessarily hold for alternative structures. Thus, there is no guarantee that
current database analytics algorithms will still provide the correct insights,
no matter what structures are chosen to organize the database. Because these
algorithms tend to be highly effective over some choices of structure, such as
that of the databases used to validate them, but not so effective with others,
database analytics has largely remained the province of experts who can find
the desired forms for these algorithms. We argue that in order to make database
analytics usable, we should use or develop algorithms that are effective over a
wide range of choices of structural organizations. We introduce the notion of
representation independence, study its fundamental properties for a wide range
of data analytics algorithms, and empirically analyze the amount of
representation independence of some popular database analytics algorithms. Our
results indicate that most algorithms are not generally representation
independent and find the characteristics of more representation independent
heuristics under certain representational shifts
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