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@@ -28,6 +28,6 @@ More precisely, we will learn to predict the estimated number of time steps requ
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> This is a really interesting paper that use learned policy and positional graph to navigate in real-world.
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> I don't have time to check the implementation, but I may assume it does not use any deep neural network for recognizing the pattern for the goal. And as the authors mentioned, the performance is sensitive to season change and illumination changes. I wonder if we can use some pattern recognition model help the model easily recognize the goal. Is there any way to do this?
> As the authors mentioned, the performance is sensitive to season change and illumination changes. I wonder if we can use some more advanced pattern recognition model help the model easily recognize the goal. Is there any way to do this?
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> How the model generalized the knowledge about the topology of the environment and know it's on the correct path if the robot is interrupted by other objects, for example, crossing bicycles or dropping leaves on the ground?