Learning control Lyapunov function to ensure stability of dynamical system-based robot reaching motions

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Benchmark for Skill Learning from Demonstration: Impact of User Experience, Task Complexity, and Start Configuration on Performance (2019, background): “We observed that ProMP, and especially CLFDM, suffer significantly when segmentation is not used.”
An Algorithmic Perspective on Imitation Learning (2018, background): “, 2013] and for an overview in reinforcement learning in robotics, please refer to Kober et al. [2013], Deisenroth et al.”
Fast diffeomorphic matching to learn globally asymptotically stable nonlinear dynamical systems (2016, unknown): “We evaluated our approach on the LASA Handwriting Dataset, similarly to [3, 4, 6].”
Learning Partially Contracting Dynamical Systems from Demonstrations (2017, methodology): “The CDSP algorithm is then compared with the DMP [2] and CLF-DM [13] algorithms.”
Learning position and orientation dynamics from demonstrations via contraction analysis (2018, methodology): “While it can encode a wide varietyofmotions,CLF-DMsuffersfromthedisadvantageof the online correction signal potentially interfering with the learned DS.”
Learning robot motions with stable dynamical systems under diffeomorphic transformations (2015, methodology): “A method that constructs Lyapunov candidates in a data-driven manner is the already mentioned weighted sum of asymmetric quadratic functions (WSAQF) [16].”