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Notes
Jan Pöppel edited this page Jun 8, 2015
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7 revisions
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Machine is called "ravenwood"
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Prediction seems to generalize a lot better when predicting differences instead of target values!!
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"Ignore" the complete worldstate. Store cases only for object states as well as interaction states.
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When predicting a new world state:
- Search for best matching OS/IS and predict those for each OS/IS in the current world state.
- Prediction score will be evaluated on each element seperately as well.
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Using constants to reduce ITM feature vector improves pushTaskSimulation performance in the sense that predicting the gripper position and the block are more aligned -> less often will the gripper be predicted to be INSIDE the block
- Transition probabilities for the ACs does not improve selection!
- Try taking cmd into account