Citation
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.

Electronic downloads

• AkkayaEtAl_PILOT_RSN_2015.pdf · application/pdf · 2945 kbytes

• HTML

  Ilge Akkaya, Shuhei Emoto, Edward A. Lee. <a
  href="http://chess.eecs.berkeley.edu/pubs/1078.html"
  >PILOT: An Actor-oriented Learning and Optimization
  Toolkit for Robotic Swarm Applications</a>, Second
  International Workshop on Robotic Sensor Networks (RSN'15),
  Cyber-Physical Systems Week 2015, 13, April, 2015.

• Plain text

  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.

• BibTeX

  @inproceedings{AkkayaEmotoLee15_PILOTActororientedLearningOptimizationToolkitForRobotic,
      author = {Ilge Akkaya and Shuhei Emoto and Edward A. Lee},
      title = {PILOT: An Actor-oriented Learning and Optimization
                Toolkit for Robotic Swarm Applications},
      booktitle = {Second International Workshop on Robotic Sensor
                Networks (RSN'15)},
      organization = {Cyber-Physical Systems Week 2015},
      day = {13},
      month = {April},
      year = {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.},
      URL = {http://chess.eecs.berkeley.edu/pubs/1078.html}
  }
  

Posted by Ilge Akkaya on 27 Oct 2014.
Groups: ptolemy
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