This is your first subject at TU München

Flashcards and summaries for This is your first subject at the TU München

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Exemplary flashcards for This is your first subject at the TU München on StudySmarter:

Name at least 5 constraints of trajectories in c-space

Exemplary flashcards for This is your first subject at the TU München on StudySmarter:

What happens if you do weight initialization with all weights to zero?

Exemplary flashcards for This is your first subject at the TU München on StudySmarter:

Explain the difference between a free path and a semi-free path

Exemplary flashcards for This is your first subject at the TU München on StudySmarter:

How do we know whether a configuration s in the free space?

Exemplary flashcards for This is your first subject at the TU München on StudySmarter:

Name 7 assumptions for the bug algorithm

Exemplary flashcards for This is your first subject at the TU München on StudySmarter:

What is the problem with complete algorithms and heuristic algorithms  in path planning tasks?

Exemplary flashcards for This is your first subject at the TU München on StudySmarter:

What is the biggest challenge with attractive and repulsive fields?

Exemplary flashcards for This is your first subject at the TU München on StudySmarter:

What does the Kalman gain calculate?    

Exemplary flashcards for This is your first subject at the TU München on StudySmarter:

What are the three assumptions of CNNs?

Exemplary flashcards for This is your first subject at the TU München on StudySmarter:

Name 3 classic path planning approaches

Exemplary flashcards for This is your first subject at the TU München on StudySmarter:

What is the difficulty with classic roadmap approaches

Exemplary flashcards for This is your first subject at the TU München on StudySmarter:

What happens if you do weight initialization with big random numbers? (10 layers with 500 neurons each, tanh and gaussian input)

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Exemplary flashcards for This is your first subject at the TU München on StudySmarter:

This is your first subject

Name at least 5 constraints of trajectories in c-space

– finite length
– Bounded curvature
– Smoothness
– Minimum length
– minimum time
– minimum energy

This is your first subject

What happens if you do weight initialization with all weights to zero?

1. There is no symmetry breaking
2. All units are going to compute the same function
3. Gradients are going to be the same

This is your first subject

Explain the difference between a free path and a semi-free path

free path lies entirely in the free space F and a semi-free path touches the boundary of one or more obstacles. 

This is your first subject

How do we know whether a configuration s in the free space?

Compute the position of the robot at that configuration in the workspace. Explicitly check for collisions with any obstacle at that position:
– if colliding, the configuration is within C-space obstacle
– Otherwise, it is in the free space

This is your first subject

Name 7 assumptions for the bug algorithm

1. Bounded world
2. Known global goal
3. Measurable distance from point x to any point y: d(x,y)
4. Unknown obstacles
5. Robot has local sensing
6. Robot is tactile (bump sensor)
7. Robot can measure travel distance

This is your first subject

What is the problem with complete algorithms and heuristic algorithms  in path planning tasks?

Complete Algorithms are slow
Heuristic Algorithms are unreliable

This is your first subject

What is the biggest challenge with attractive and repulsive fields?

You can create local minima which would stop the robot

This is your first subject

What does the Kalman gain calculate?    

The Kalman gain calculates the proportion of measuret variables and predicted variables for the new prediction 

This is your first subject

What are the three assumptions of CNNs?

1. nearby pixels are correlated
2. weight sharing -> interesting features all over the place
3. Translational invariance

This is your first subject

Name 3 classic path planning approaches

Roadmap
Cell decomposition
Potential field

This is your first subject

What is the difficulty with classic roadmap approaches

Running time increases exponentially with the dimensions of the c-space

several variants of the path planning problem have been proven to be PSPACE-hard

This is your first subject

What happens if you do weight initialization with big random numbers? (10 layers with 500 neurons each, tanh and gaussian input)

Output saturates quickly because the tanh function saturated for big numbers (either -1 or 1)

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