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Learning Trajectory Cost Functions From Human Preferences

The code is designed to support the research in LfHP. To run the code use pr2_hereshow_pick_traj.launch file.

The code implements the GUI to capture the human preferences by displaying precomputed trajectories for a particular environment in RViz and allowing a user to choose between the two, which one is better. The ranking of trajectories is computed based on the pairwise human decisions. The decision trees are used on a set of features to compute the cost function.

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  • Python 84.1%
  • MATLAB 15.9%