Autonomous Online Learning of Reaching Behavior in a humanoid Robot

By Lorenzo Jamone, Lorenzo Natale, F. Nori, Giorgio Metta, Giulio Sandini. 2012

55 citations recorded by the source corpus.

Subfields: Biomedical Engineering, 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 Biomedical Engineering

1. Giulio Sandini
2. Lei Tai

In Control and Systems Engineering

1. Alexandre Bernardino
2. Lorenzo Jamone
3. Pedro Vicente
4. Giulio Sandini
5. Lorenzo Natale
6. Lei Tai
7. Manuel Lopes
8. Pierre-Yves Oudeyer

Citations in the corpus

• Robotic Hand Pose Estimation Based on Stereo Vision and GPU-enabled Internal Graphical Simulation (2016, background): “Online solutions have been studied for example in [18, 21, 42], in which visual markers are used to easily detect the hand position.”
• Autonomous online generation of a motor representation of the workspace for intelligent whole-body reaching (2014, methodology): “For more details about the autonomous online learning of the reaching controller the reader should refer to [39].”
• A robot exploration strategy based on Q-learning network (2016, background): “Reinforcement learning was also proved to improve the motion behaviour of a humanoid robot to react for visually iden-tified objects substantially [10], by building an autonomous strategy with little prior knowledge.”