{'fraction_non_alphanumeric': 0.0282574568288854, 'fraction_numerical': 0.006279434850863423, 'mean_word_length': 4.646017699115045, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Solving navigation tasks with learned Teleo-Reactive Programs

Programming a robot to perform tasks in dynamic environments is a complex process. Teleo-reactive programs (TRPs) (S. Benson and N.J. Nilsson, 1995) have proved to be an effective framework to continuously perform a set of actions to achieve particular goals and react in the presence of unexpected events, however, their definition is a difficult and time-consuming process. In this paper, it is shown how a robot can learn TRPs from human guided traces. A user guides a robot to perform a task and the robot learns how to perform such task in similar dynamic environments.