{'fraction_non_alphanumeric': 0.022774327122153208, 'fraction_numerical': 0.004140786749482402, 'mean_word_length': 4.342541436464089, '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}}
A Deep Regression Model for Safety Control in Visual Servoing Applications

In Human-Robot Interaction scenarios, a human often needs to interact or closely working with objects and/or the robot. Hence the safety aspect needs to be taken care of in the Human-Robot Interaction scenarios. In this paper, we apply a deep learning approach to learning an optimal repulsive pose. The end effector of the robot will move the optimal repulsive pose if the human hand is too close to the end effector. We use a ResNet based deep regression model to learn the weights between the input i.e. the hand position + Tool Center Point position and output i.e. the repulsive pose. We evaluate the model with different readouts and loss functions. With the Fully Connected readout, the Mean absolute Error in the x, y and z directions are between 7.4 mm and 7.7 mm. The model inference time is also smaller than the computation time of calculating the optimal repulsive pose online.