ethz-asl / ai_for_robotics

Programming Exercises Accompanying the Lecture "Artificial Intelligence for Robotics"

Artificial Intelligence for Robotics

A python cheat sheet containing the basics needed for this course can be found here: https://perso.limsi.fr/pointal/_media/python:cours:mementopython3-english.pdf

The documentation of SciPy can be found here: http://scipy-cookbook.readthedocs.io/index.html

Exercise overview

• 0_1_python_introduction_exercise: basic python examples
• 0_2_python_intro_applications: python applications (linear regression, optimization)
• 1_0_probability_ml_basics: Probability recap and machine learning basics
• 2_0_regression_pgm: Regression and probabilistic graphical models
• 3_0_pgo_icp: Pose graph optimization and iterative closest point
• 4_0_pca_kmeans_svm: Principal Component Analysis (PCA), k-means clustering, Support Vector Machine (SVM).
• 5_deep_learning: Backpropagation, Convolutional Neural Networks (CNNs), Deep Reinforcement Learning (RL).

ai_for_robotics's Issues

From perception to decision: A data-driven approach to end-to-end motion planning for autonomous ground robots

Respected Mr Pfeiffer, I have been honored to read your article named by "From perception to decision: A data-driven approach to end-to-end motion planning for autonomous ground robots" in the ICRA this year. I am
very impressed with the neural network-based motion planner you present in the article. Moreover, I am also interested in how to implement the model in my robot. To be honest, I have learned TensorFlow framework recently, but to complete a model like that in short order is really difficult to me, a beginner. So would you like to share the model and the programs to more learners like me? Thank you very much!