20,235 research outputs found
Cooperative Adaptive Control for Cloud-Based Robotics
This paper studies collaboration through the cloud in the context of
cooperative adaptive control for robot manipulators. We first consider the case
of multiple robots manipulating a common object through synchronous centralized
update laws to identify unknown inertial parameters. Through this development,
we introduce a notion of Collective Sufficient Richness, wherein parameter
convergence can be enabled through teamwork in the group. The introduction of
this property and the analysis of stable adaptive controllers that benefit from
it constitute the main new contributions of this work. Building on this
original example, we then consider decentralized update laws, time-varying
network topologies, and the influence of communication delays on this process.
Perhaps surprisingly, these nonidealized networked conditions inherit the same
benefits of convergence being determined through collective effects for the
group. Simple simulations of a planar manipulator identifying an unknown load
are provided to illustrate the central idea and benefits of Collective
Sufficient Richness.Comment: ICRA 201
Learning theories reveal loss of pancreatic electrical connectivity in diabetes as an adaptive response
Cells of almost all solid tissues are connected with gap junctions which
permit the direct transfer of ions and small molecules, integral to regulating
coordinated function in the tissue. The pancreatic islets of Langerhans are
responsible for secreting the hormone insulin in response to glucose
stimulation. Gap junctions are the only electrical contacts between the
beta-cells in the tissue of these excitable islets. It is generally believed
that they are responsible for synchrony of the membrane voltage oscillations
among beta-cells, and thereby pulsatility of insulin secretion. Most attempts
to understand connectivity in islets are often interpreted, bottom-up, in terms
of measurements of gap junctional conductance. This does not, however explain
systematic changes, such as a diminished junctional conductance in type 2
diabetes. We attempt to address this deficit via the model presented here,
which is a learning theory of gap junctional adaptation derived with analogy to
neural systems. Here, gap junctions are modelled as bonds in a beta-cell
network, that are altered according to homeostatic rules of plasticity. Our
analysis reveals that it is nearly impossible to view gap junctions as
homogeneous across a tissue. A modified view that accommodates heterogeneity of
junction strengths in the islet can explain why, for example, a loss of gap
junction conductance in diabetes is necessary for an increase in plasma insulin
levels following hyperglycemia.Comment: 15 pages, 5 figures. To appear in PLoS One (2013
Distributed Graph Automata and Verification of Distributed Algorithms
Combining ideas from distributed algorithms and alternating automata, we
introduce a new class of finite graph automata that recognize precisely the
languages of finite graphs definable in monadic second-order logic. By
restricting transitions to be nondeterministic or deterministic, we also obtain
two strictly weaker variants of our automata for which the emptiness problem is
decidable. As an application, we suggest how suitable graph automata might be
useful in formal verification of distributed algorithms, using Floyd-Hoare
logic.Comment: 26 pages, 6 figures, includes a condensed version of the author's
Master's thesis arXiv:1404.6503. (This version of the article (v2) is
identical to the previous one (v1), except for minor changes in phrasing.
A Coordinate Descent Primal-Dual Algorithm and Application to Distributed Asynchronous Optimization
Based on the idea of randomized coordinate descent of -averaged
operators, a randomized primal-dual optimization algorithm is introduced, where
a random subset of coordinates is updated at each iteration. The algorithm
builds upon a variant of a recent (deterministic) algorithm proposed by V\~u
and Condat that includes the well known ADMM as a particular case. The obtained
algorithm is used to solve asynchronously a distributed optimization problem. A
network of agents, each having a separate cost function containing a
differentiable term, seek to find a consensus on the minimum of the aggregate
objective. The method yields an algorithm where at each iteration, a random
subset of agents wake up, update their local estimates, exchange some data with
their neighbors, and go idle. Numerical results demonstrate the attractive
performance of the method. The general approach can be naturally adapted to
other situations where coordinate descent convex optimization algorithms are
used with a random choice of the coordinates.Comment: 10 page
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