A Framework for Learning and Control in Intelligent Humanoid Robots
By Oliver Brock, Andrew H. Fagg, R. Grupen, Robert Platt Jr., Michael T. Rosenstein, John Sweeney. 2005
33 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Control and Systems Engineering, Reinforcement Learning, Robotics & Control
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
- George Dimitri Konidaris
- Scott Niekum
- Oliver Kroemer
- Bruno Castro da Silva
- Sergey Levine
- Andrew Barto
- Jan Peters
- Chelsea Finn
In Control and Systems Engineering
- George Dimitri Konidaris
- Scott Niekum
- Oliver Kroemer
- Andrew Barto
- Bruno Castro da Silva
- Jan Peters
- Sergey Levine
- Huizhen Yu
In Reinforcement Learning
- George Dimitri Konidaris
- Scott Niekum
- Bruno Castro da Silva
- Oliver Kroemer
- Andrew Barto
- Sergey Levine
- Jan Peters
- Shie Mannor
In Robotics & Control
- George Dimitri Konidaris
- Scott Niekum
- Andrew Barto
- Bruno Castro da Silva
- Oliver Kroemer
- Jan Peters
- Sergey Levine
- Gerhard Neumann
Citations in the corpus
- Learning Parameterized Skills (2012, background): “Grupen’s motor control framework aims at constructing hierarchical closed-loop motor control plans [13].”
- A Review of Robot Learning for Manipulation: Challenges, Representations, and Algorithms (2019, methodology): “Several approaches have been applied here, for example using the status of the skills available to the robot as a state space [40, 143], finding representations that minimize planning loss [225, 177, 394, 395], using qualitative state abstractions [271] and constructing compact MDPs that are provabl”