A Dynamical System-based Approach to Modeling Stable Robot Control Policies via Imitation Learning

By K. Zadeh, Seyed Mohammad. 2012

22 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Biomedical Engineering, Control and Systems Engineering

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. René Felix Reinhart
2. Klaus Neumann
3. Andre Lemme
4. Jochen J. Steil
5. Harish Chaandar Ravichandar
6. Ashwin P. Dani
7. J. Urgen Schmidhuber
8. Peter Tino

In Biomedical Engineering

1. René Felix Reinhart
2. Klaus Neumann
3. Andre Lemme
4. Jochen J. Steil

In Control and Systems Engineering

1. Jochen J. Steil
2. Klaus Neumann
3. René Felix Reinhart
4. Andre Lemme
5. Harish Chaandar Ravichandar
6. Ashwin P. Dani
7. Iman Salehi

Citations in the corpus

• Learning robot motions with stable dynamical systems under diffeomorphic transformations (2015, methodology): “Another approach that allows learning larger sets of demonstrations accurately is the recently developed con-trol Lyapunov function - dynamic movements (CLF-DM) approach 3 which was published in [15, 16].”
• Neural learning of vector fields for encoding stable dynamical systems (2014, methodology): “The SEDS-II approach [18] tackles this issue by correcting the dynamical system online according to a Lyapunov function adapted to the data.”