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Bilateral Control-Based Imitation Learning for Velocity-Controlled Robot

Machine learning is now playing important role in robotic object manipulation. In addition, force control is necessary for manipulating various objects to achieve robustness against perturbations of configurations and stiffness. The author's group revealed that fast and dynamic object manipulation with force control can be obtained by bilateral control-based imitation learning. However, the method is applicable only in robots that can control torque, while it is not applicable in robots that can only follow position or velocity commands like many commercially available robots. Then, in this research, a way to implement bilateral control-based imitation learning to velocity-controlled robots is proposed. The validity of the proposed method is experimentally verified by a mopping task.

preprint2021arXivOpen access
Sho Sakaino
Systems and Controleess.SYRobotics
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Open access1 author3 topics
Imported metadata coverageMissing code, dataset, citation and institution fields are tracked without dominating the paper.Details
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Sho Sakaino

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