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Robots are expected to replace menial tasks such as housework. Some of thesetasks include nonprehensile manipulation performed without grasping objects.Nonprehensile manipulation is very difficult because it requires consideringthe dynamics of environments and objects. Therefore imitating complex behaviorsrequires a large number of human demonstrations. In this study, aself-supervised learning that considers dynamics to achieve variable speed fornonprehensile manipulation is proposed. The proposed method collects andfine-tunes only successful action data obtained during autonomous operations.By fine-tuning the successful data, the robot learns the dynamics among itself,its environment, and objects. We experimented with the task of scooping andtransporting pancakes using the neural network model trained on 24human-collected training data. The proposed method significantly improved thesuccess rate from 40.2% to 85.7%, and succeeded the task more than 75% forother objects.

Imitation Learning for Nonprehensile Manipulation through Self-Supervised Learning Considering Motion Speed