# Difference between revisions of "VVV09 Kinematic, my friend"

From Wiki for iCub and Friends

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== FAQ like == | == FAQ like == | ||

− | + | '''Why learn the model, we already have an analytic method ?''' | |

− | Why learn the model, we already have an analytic method ? | ||

* A human like approach. | * A human like approach. | ||

* To validate the feasibility of such kind of learning. | * To validate the feasibility of such kind of learning. | ||

− | Why the forward model ? | + | '''Why the forward model ?''' |

* To keep all the information of the model (i.e infinity of pseudo-inverse of the Jacobian, null space computation). | * To keep all the information of the model (i.e infinity of pseudo-inverse of the Jacobian, null space computation). | ||

− | Why the kinematic ? | + | '''Why the kinematic ?''' |

* Because the geometric model is not easily invertible. | * Because the geometric model is not easily invertible. | ||

− | What kind of algorithm is using ? | + | '''What kind of algorithm is using ?''' |

[[Image:pc_learning.jpg|right|192px|thumbnail|[[The learning attitude]]!]] | [[Image:pc_learning.jpg|right|192px|thumbnail|[[The learning attitude]]!]] | ||

* LWPR, (Locally Weighted Projection Regression) [Vijayakumar 2005]. A powerfull online learning algorithm based on multiple linear regression computation in the space the most adapted (PLS). | * LWPR, (Locally Weighted Projection Regression) [Vijayakumar 2005]. A powerfull online learning algorithm based on multiple linear regression computation in the space the most adapted (PLS). | ||

− | What are the results ? | + | '''What are the results ?''' |

* The model is learned and uses "successfully" on the robot. | * The model is learned and uses "successfully" on the robot. | ||

− | What about the "keep all the information" ? | + | '''What about the "keep all the information" ?''' |

* With the learned model we are able to control two end-effectors with a priority on one of them. | * With the learned model we are able to control two end-effectors with a priority on one of them. | ||

− | So, what did you do here ? | + | '''So, what did you do here ?''' |

* I tried to learn the model ... without the model but with the vision. | * I tried to learn the model ... without the model but with the vision. | ||

− | And ? | + | '''And ?''' |

* I have learned the model by attended motor babbling (As we play with the "end effector" of a baby). | * I have learned the model by attended motor babbling (As we play with the "end effector" of a baby). | ||

* I "wanted" to be able to track two objects (my end effector and another object ... But it is apparently not so easy :p) in order to complete the learning of the model still as if we were playing with a child. | * I "wanted" to be able to track two objects (my end effector and another object ... But it is apparently not so easy :p) in order to complete the learning of the model still as if we were playing with a child. |

## Revision as of 11:27, 29 July 2009

The aim subject is the learning of the forward kinematic model.

## FAQ like

**Why learn the model, we already have an analytic method ?**

- A human like approach.
- To validate the feasibility of such kind of learning.

**Why the forward model ?**

- To keep all the information of the model (i.e infinity of pseudo-inverse of the Jacobian, null space computation).

**Why the kinematic ?**

- Because the geometric model is not easily invertible.

**What kind of algorithm is using ?**

- LWPR, (Locally Weighted Projection Regression) [Vijayakumar 2005]. A powerfull online learning algorithm based on multiple linear regression computation in the space the most adapted (PLS).

**What are the results ?**

- The model is learned and uses "successfully" on the robot.

**What about the "keep all the information" ?**

- With the learned model we are able to control two end-effectors with a priority on one of them.

**So, what did you do here ?**

- I tried to learn the model ... without the model but with the vision.

**And ?**

- I have learned the model by attended motor babbling (As we play with the "end effector" of a baby).
- I "wanted" to be able to track two objects (my end effector and another object ... But it is apparently not so easy :p) in order to complete the learning of the model still as if we were playing with a child.