Active learning using mean shift optimization for robot grasping

By Oliver Kroemer, Renaud Detry, Justus H. Piater, Jan Peters. 2009

34 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, 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 Artificial Intelligence

1. Jan Peters
2. Sergey Levine
3. Pieter Abbeel
4. J. Bagnell
5. Jens Kober
6. Chelsea Finn
7. Gerhard Neumann
8. Duy Nguyen-Tuong

In Computational Theory and Mathematics

1. Sergey Levine
2. J. Bagnell
3. Jan Peters
4. Jens Kober
5. Duy Nguyen-Tuong
6. Pieter Abbeel
7. J. Urgen Schmidhuber
8. Gerhard Neumann

In Control and Systems Engineering

1. Jan Peters
2. Jens Kober
3. J. Bagnell
4. Sergey Levine
5. Duy Nguyen-Tuong
6. Pieter Abbeel
7. Gerhard Neumann
8. Marc Peter Deisenroth

Citations in the corpus

• Reinforcement learning in robotics: A survey (2013, background): “Grasping locations can be learned by approximating the rewards with a GPR, and trying candidates with predicted high rewards (Kroemer et al., 2009), resulting in an active learning approach.”
• Model learning for robot control: a survey (2011, background): “2007) or grasping (Kroemer et al. 2009), active learning can be used to label training data for model learning.”