Thursday, 5th of May 2022, 12:00 – 1:00

Extrapolation in robot skill generalization


Hector Villeda - researcher at IIS


Imitation learning approaches achieve good generalization within the range of the training data, but tend to generate unpredictable motions when querying outside this range. We present a novel approach to imitation learning with enhanced extrapolation capabilities that exploits the so-called Equation Learner Networks (EQLN). Unlike conventional approaches, EQLN use supervised learning to fit a set of analytical expressions that allows them to extrapolate beyond the range of the training data. We augment the task demonstrations with a set of task dependent parameters representing spacial properties of each motion and use them to train the EQLN. At run time, the features are used to query the EQLN and generate the corresponding robot trajectory. The set of features encodes kinematic constraints of the task. We validate the results of our approach on manipulation tasks where it is important to preserve the shape of the motion in the extrapolation domain. Our approach is also compared with existing state-of-the-art approaches, either in simulation and real setup environments.


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