Fast diffeomorphic matching to learn globally asymptotically stable nonlinear dynamical systems

By Nicolas Perrin, Philipp Schlehuber-Caissier. 2016

66 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Cognitive Neuroscience, 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. Harish Chaandar Ravichandar
2. Sonia Chernova
3. Seyed Reza Ahmadzadeh
4. Athanasios S. Polydoros
5. Muhammad Asif Rana
6. Aude Gemma Billard
7. Daphne Chen
8. Jacob Williams

In Cognitive Neuroscience

1. Sonia Chernova
2. Aude Gemma Billard
3. Harish Chaandar Ravichandar
4. Seyed Reza Ahmadzadeh
5. Athanasios S. Polydoros
6. Muhammad Asif Rana
7. Ming Yuan
8. Yi Lin

In Control and Systems Engineering

1. Harish Chaandar Ravichandar
2. Sonia Chernova
3. Seyed Reza Ahmadzadeh
4. Athanasios S. Polydoros
5. Muhammad Asif Rana
6. Aude Gemma Billard
7. Daphne Chen
8. Jacob Williams

Citations in the corpus

• Benchmark for Skill Learning from Demonstration: Impact of User Experience, Task Complexity, and Start Configuration on Performance (2019, background): “This behavior is in fact expected for all LfD approaches which learn first-order dynamical systems from demonstrations [2], [4]–[6].”
• Recent Advances in Robot Learning from Demonstration (2020, background): “For instance, numerous dynamical-systems-based trajectory-learning methods provide strong convergence guarantees (70, 78, 80, 82, 99).”