Imitation learning for a continuum trunk robot
نویسندگان
چکیده
The paper applies learning from demonstration (LfD) for high-level trajectory planning and movement control of the Bionic Handling Assistant (BHA) robot. For such soft continuum robot with mechanical elasticity and complex dynamics it is difficult to use kinesthetic teaching to collect demonstration data. We propose to use an active compliant controller to this aim and record both position and orientation of the BHA’s end-effector. Subsequently, this data is then encoded with a state-of-the-art task-parameterized probabilistic Gaussian mixture model and its performance and generalization is experimentally evaluated.
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