Generalization of Task Parameterized Dynamical Systems using Gaussian Process Transportation
arxiv(2024)
摘要
Learning from Interactive Demonstrations has revolutionized the way
non-expert humans teach robots. It is enough to kinesthetically move the robot
around to teach pick-and-place, dressing, or cleaning policies. However, the
main challenge is correctly generalizing to novel situations, e.g., different
surfaces to clean or different arm postures to dress. This article proposes a
novel task parameterization and generalization to transport the original robot
policy, i.e., position, velocity, orientation, and stiffness. Unlike the state
of the art, only a set of points are tracked during the demonstration and the
execution, e.g., a point cloud of the surface to clean. We then propose to fit
a non-linear transformation that would deform the space and then the original
policy using the paired source and target point sets. The use of function
approximators like Gaussian Processes allows us to generalize, or transport,
the policy from every space location while estimating the uncertainty of the
resulting policy due to the limited points in the task parameterization point
set and the reduced number of demonstrations. We compare the algorithm's
performance with state-of-the-art task parameterization alternatives and
analyze the effect of different function approximators. We also validated the
algorithm on robot manipulation tasks, i.e., different posture arm dressing,
different location product reshelving, and different shape surface cleaning.
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