Perception and Developmental Learning of Affordances in Autonomous Robots

By Lucas Paletta, Gerald Fritz, Florian Kintzler, Jörg Irran, Georg Dorffner. 2007

15 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Cognitive Neuroscience, 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. Sergey Levine
2. Jan Peters
3. Pieter Abbeel
4. Jens Kober
5. J. Bagnell
6. Chelsea Finn
7. Gerhard Neumann
8. Vladlen Koltun

In Cognitive Neuroscience

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

In Control and Systems Engineering

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

Citations in the corpus

• Reinforcement learning in robotics: A survey (2013, background): “The state-space can also be based on vastly different features, such as positions, shapes, and colors, when learning object affordances (Paletta et al., 2007) where both the discrete sets and the mapping from sensor values to the discrete values need to be crafted.”