{'fraction_non_alphanumeric': 0.02040816326530612, 'fraction_numerical': 0.004081632653061225, 'mean_word_length': 5.411764705882353, '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}}
Contact force estimation for backdrivable robotic manipulators with coupled friction

A hybrid model/learning-based dynamic equation has been developed for robots in order to take advantage of the attractive features of both techniques. The regular dynamic equation is used to model the ideal behavior, while wavelets are used to learn the joint friction. The friction may be coupled, such that it is a function of the velocity of multiple joints. Furthermore, a system identification method has been designed to improve contact force estimation, using manual hand excitation in addition to motor excitation. The hybrid dynamic equation was implemented for the PHANTOM Omni haptic device (SensAble Technologies, MA, USA), and experiments were performed to compare using manual excitation with using motor excitation only. The results showed that manual excitation improved contact force estimation performance, with improvements in the relative RMS values ranging from 21% to 35%.