• Corpus ID: 228376399

Structured Policy Representation: Imposing Stability in arbitrarily conditioned dynamic systems

  title={Structured Policy Representation: Imposing Stability in arbitrarily conditioned dynamic systems},
  author={Julen Urain and Davide Tateo and Tianyu Ren and Jan Peters},
We present a new family of deep neural network-based dynamic systems. The presented dynamics are globally stable and can be conditioned with an arbitrary context state. We show how these dynamics can be used as structured robot policies. Global stability is one of the most important and straightforward inductive biases as it allows us to impose reasonable behaviors outside the region of the demonstrations. 

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Learning by Demonstration

  • S. Schaal
  • Education, Computer Science
    Encyclopedia of Machine Learning and Data Mining
  • 1996
In an implementation of pole balancing on a complex anthropomorphic robot arm, it is demonstrated that, when facing the complexities of real signal processing, model-based reinforcement learning offers the most robustness for LQR problems.