Computer Science > Robotics
[Submitted on 21 Jun 2022 (v1), last revised 22 Jun 2022 (this version, v2)]
Title:Imitation Learning for Nonprehensile Manipulation through Self-Supervised Learning Considering Motion Speed
View PDFAbstract:Robots are expected to replace menial tasks such as housework. Some of these tasks include nonprehensile manipulation performed without grasping objects. Nonprehensile manipulation is very difficult because it requires considering the dynamics of environments and objects. Therefore imitating complex behaviors requires a large number of human demonstrations. In this study, a self-supervised learning that considers dynamics to achieve variable speed for nonprehensile manipulation is proposed. The proposed method collects and fine-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 and transporting pancakes using the neural network model trained on 24 human-collected training data. The proposed method significantly improved the success rate from 40.2% to 85.7%, and succeeded the task more than 75% for other objects.
Submission history
From: Yuki Saigusa [view email][v1] Tue, 21 Jun 2022 12:15:21 UTC (10,055 KB)
[v2] Wed, 22 Jun 2022 12:19:12 UTC (10,050 KB)
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