Parabolic movement primitives and cortical states: merging optimality with geometric invariance

By F. Polyakov, Eran Stark, Rotem Drori, Moshe Abeles, Tamar Flash. 2009

51 citations recorded by the source corpus.

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Models of human movement: Trajectory planning and inverse kinematics studies (2013, unknown): “identified movement paths whose generation both obeys the two-thirds power law and yields minimum-jerk trajectories [39,40].”
Segmenting sign language into motor primitives with Bayesian binning (2013, methodology): “…in that paper appears rather different from ours: while we use a marginal likelihood based criterion which follows from the polynomial observation model, the authors of Polyakov et al. (2009b) ﬁrst extracted from the recorded data portions corresponding to active movement and others of rest.”
Geometrical Invariance and Smoothness Maximization for Task-Space Movement Generation (2016, methodology): “In Section VI, we discuss our MG algorithm and the potential of defining movement complexity through optimization criteria, such as the minimum jerk model [3], and based on geometrical invariance (which we introduced in the context of motor control in [2] and [25]).”
Movement Timing and Invariance Arise from Several Geometries (2009, background): “For instance, as suggested in [51,52], parabolic arcs are selected in affine geometry, because they remain invariant under several families of transformation.”