34,600 research outputs found
A Concurrency Control Method Based on Commitment Ordering in Mobile Databases
Disconnection of mobile clients from server, in an unclear time and for an
unknown duration, due to mobility of mobile clients, is the most important
challenges for concurrency control in mobile database with client-server model.
Applying pessimistic common classic methods of concurrency control (like 2pl)
in mobile database leads to long duration blocking and increasing waiting time
of transactions. Because of high rate of aborting transactions, optimistic
methods aren`t appropriate in mobile database. In this article, OPCOT
concurrency control algorithm is introduced based on optimistic concurrency
control method. Reducing communications between mobile client and server,
decreasing blocking rate and deadlock of transactions, and increasing
concurrency degree are the most important motivation of using optimistic method
as the basis method of OPCOT algorithm. To reduce abortion rate of
transactions, in execution time of transactions` operators a timestamp is
assigned to them. In other to checking commitment ordering property of
scheduler, the assigned timestamp is used in server on time of commitment. In
this article, serializability of OPCOT algorithm scheduler has been proved by
using serializability graph. Results of evaluating simulation show that OPCOT
algorithm decreases abortion rate and waiting time of transactions in compare
to 2pl and optimistic algorithms.Comment: 15 pages, 13 figures, Journal: International Journal of Database
Management Systems (IJDMS
Role Playing Learning for Socially Concomitant Mobile Robot Navigation
In this paper, we present the Role Playing Learning (RPL) scheme for a mobile
robot to navigate socially with its human companion in populated environments.
Neural networks (NN) are constructed to parameterize a stochastic policy that
directly maps sensory data collected by the robot to its velocity outputs,
while respecting a set of social norms. An efficient simulative learning
environment is built with maps and pedestrians trajectories collected from a
number of real-world crowd data sets. In each learning iteration, a robot
equipped with the NN policy is created virtually in the learning environment to
play itself as a companied pedestrian and navigate towards a goal in a socially
concomitant manner. Thus, we call this process Role Playing Learning, which is
formulated under a reinforcement learning (RL) framework. The NN policy is
optimized end-to-end using Trust Region Policy Optimization (TRPO), with
consideration of the imperfectness of robot's sensor measurements. Simulative
and experimental results are provided to demonstrate the efficacy and
superiority of our method
Neural Task Programming: Learning to Generalize Across Hierarchical Tasks
In this work, we propose a novel robot learning framework called Neural Task
Programming (NTP), which bridges the idea of few-shot learning from
demonstration and neural program induction. NTP takes as input a task
specification (e.g., video demonstration of a task) and recursively decomposes
it into finer sub-task specifications. These specifications are fed to a
hierarchical neural program, where bottom-level programs are callable
subroutines that interact with the environment. We validate our method in three
robot manipulation tasks. NTP achieves strong generalization across sequential
tasks that exhibit hierarchal and compositional structures. The experimental
results show that NTP learns to generalize well to- wards unseen tasks with
increasing lengths, variable topologies, and changing objectives.Comment: ICRA 201
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