Latent spaces for dynamic movement primitives

By Sebastian Bitzer, Sethu Vijayakumar. 2009

57 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Control and Systems Engineering

How researchers see this paper

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

In Computer Vision and Pattern Recognition

1. George Dimitri Konidaris
2. Scott Niekum
3. Bhaskara Marthi
4. Pierre-Yves Oudeyer
5. Andrew Barto
6. Sachin Chitta
7. Adrien Baranes

In Control and Systems Engineering

1. Pierre-Yves Oudeyer
2. George Dimitri Konidaris
3. Adrien Baranes
4. Scott Niekum
5. Andrew Barto
6. Bhaskara Marthi
7. Sarah Osentoski
8. Sachin Chitta

Citations in the corpus

• Active learning of inverse models with intrinsically motivated goal exploration in robots (2013, positive): “…little assumption about the low-level regression algorithms are made in the SAGG-RIAC architecture, parameterized models allowing to control memory requirements such as Neural networks, Support Vector Regression, Gaussian Process Regression could instead be considered [1], such as in [68,51,69,70].”
• Learning grounded finite-state representations from unstructured demonstrations (2015, background): “However, it is well known that the average of solutions to a problem is often not itself a solution, making this problem non-trivial, although some approaches have been explored (Coates et al., 2008; Bitzer and Vijayakumar, 2009).”