Learning Augmented Joint-space Task-oriented Dynamical Systems
In this letter, we propose an asymptotically stable joint-space dynamical system (DS) that captures desired behaviors in joint-space while converging toward a task-space attractor in both position and orientation. To encode joint-space behaviors while meeting the stability criteria, we propose a DS constructed as a linear parameter varying system combining different behavior synergies and 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 semidefinite optimization problem that minimizes the joint velocity prediction error from the demonstrations. Our proposed approach is empirically validated on a variety of motions that reach a target in position and orientation, while following a desired joint-space behavior.
Video of Robot Experiments
Following we list all of the code repositories made available for this project, including:
 Yonadav Shavit, Figueroa, N., Mirrazavi Salehian, S. S. and Billard, A. (2018) Learning Augmented Joint-Space Task-Oriented Dynamical Systems: A Linear Parameter Varying and Synergetic Control Approach. IEEE Robotics and Automation Letters (RA-L). link