PILOT: An Actor-oriented Learning and Optimization Toolkit for Robotic Swarm Applications
Ilge Akkaya, Shuhei Emoto, Edward A. Lee
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Ilge Akkaya, Shuhei Emoto, Edward A. Lee. "PILOT: An Actor-oriented Learning and Optimization Toolkit for Robotic Swarm Applications". Second International Workshop on Robotic Sensor Networks (RSN'15), Cyber-Physical Systems Week 2015, 13, April, 2015.
Abstract
We present PILOT (Ptolemy Inference, Learning, and Optimization Toolkit), an actor-oriented machine learning and optimization toolkit that is designed for developing data intensive distributed applications for sensor networks. We de- fine an actor interface that bridges state-space models for robotic control problems and a collection of machine learning and optimization algorithms, then demonstrate how the framework leverages programmability of sophisticated distributed robotic applications on streaming data. As a case study, we consider a cooperative target tracking scenario and study how the framework enables adaptation and implementation of control policies and simulation within environmental constraints by presenting actor-oriented abstractions that enable application developers to build state-space aware machine learning and optimization actors.
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