Motor Learning at Intermediate Reynolds Number: Experiments with Policy Gradient on the Flapping Flight of a Rigid Wing

By John W. Roberts, Jun Zhang, Russ Tedrake. 2010

24 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computational Mechanics, Statistical and Nonlinear Physics

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

1. Jan Peters
2. Olivier Sigaud
3. Russ Tedrake
4. John W. Roberts
5. Sergey Levine
6. Nicolas Le Roux

In Computational Mechanics

1. Jan Peters
2. Olivier Sigaud
3. Russ Tedrake
4. John W. Roberts

In Statistical and Nonlinear Physics

1. Jan Peters
2. Olivier Sigaud

Citations in the corpus

• From Motor Learning to Interaction Learning in Robots (2010, methodology): “Finally, the contribution from [26] describes a nice application of a model-free reinforcement learning-based control methodology, based on an optimized policy gradient algorithm, to the control of an experimental system dedicated to the study of flapping wing flight.”
• Feedback controller parameterizations for Reinforcement Learning (2011, background): “Most uses of learning which have been successful on hardware involved learning an open-loop trajectory or control tape (see, for example [2], [13], [20], [23]).”