Klaus Neumann

Control and Systems Engineering · 2013–2015

A researcher in the Science corpus: 3 papers; trusts 10 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Control and Systems Engineering — 17 citations into it; #2,132 of 8,257 by trust here, #4,262 by citations
• Artificial Intelligence — 15 citations into it; #15,105 of 35,345 by trust here, #27,551 by citations
• Biomedical Engineering — 6 citations into it
• Reinforcement Learning — 6 citations into it
• Probabilistic & Bayesian Methods — 5 citations into it

Whom Klaus Neumann trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Seyed Mohammad Khansari-Zadeh
2. Aude Gemma Billard
3. K. Zadeh
4. Stefan Schaal
5. Auke Jan Ijspeert
6. Seyed Mohammad
7. Sylvain Calinon
8. Andre Lemme
9. 採編典藏組
10. Jun Nakanishi

Who trusts Klaus Neumann overall

1. Harish Chaandar Ravichandar
2. Ashwin P. Dani
3. Iman Salehi
4. Andre Lemme
5. Jochen J. Steil
6. Athanasios S. Polydoros
7. Yaron Meirovitch
8. Sonia Chernova
9. Nicolas Perrin

Papers from Klaus Neumann's perspective

Ordered by how strongly the researchers Klaus Neumann trusts endorse each paper. Same corpus, a different order for every researcher.

1. Is imitation learning the route to humanoid robots?
2. Learning Attractor Landscapes for Learning Motor Primitives
3. Task-Specific Generalization of Discrete and Periodic Dynamic Movement Primitives
4. BM: An iterative algorithm to learn stable non-linear dynamical systems with Gaussian mixture models
5. Robot Programming by Demonstration
6. Movement imitation with nonlinear dynamical systems in humanoid robots
7. Dynamical Movement Primitives: Learning Attractor Models for Motor Behaviors
8. Learning Stable Nonlinear Dynamical Systems With Gaussian Mixture Models
9. Learning from demonstration and adaptation of biped locomotion
10. Spin Torque and Force due to Current for General Spin Textures(Condensed matter: electronic structure and electrical, magnetic, and optical properties)

Their papers

• Learning robot motions with stable dynamical systems under diffeomorphic transformations (2015) — 104 citations
• Neural learning of vector fields for encoding stable dynamical systems (2014) — 56 citations
• Neural learning of stable dynamical systems based on data-driven Lyapunov candidates (2013) — 61 citations