Learning Augmented Joint-Space Task-Oriented Dynamical Systems:A Linear Parameter Varying and Synergetic Control Approach

In this paper, we propose an asymptotically stable joint-space dynamical system that captures desired behaviors in joint-space while stably converging towards a task-space attractor. To encode joint-space behaviors while meeting the stability criteria, the dynamical system is constructed as a Linear Parameter Varying (LPV) system combining different motor synergies, and we provide a method for learning these synergy matrices from demonstrations. Specifically, we use dimensionality reduction to find a low-dimensional embedding space for modulating joint synergies, and then estimate the parameters of the corresponding synergies by solving a convex semi-definite optimization problem that minimizes the joint velocity prediction error from the demonstrations. Our proposed approach is empirically validated on a variety of motions.


Published in:
IEEE Robotics and Automation Letters (RA-L)
Year:
May 06 2018
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 Record created 2018-05-06, last modified 2018-12-03

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