Learning position and orientation dynamics from demonstrations via contraction analysis

By Harish Chaandar Ravichandar, Ashwin P. Dani. 2018

45 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Control and Systems Engineering, Mechanical 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 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

In Mechanical Engineering

1. Sonia Chernova
2. Aude Gemma Billard
3. Athanasios S. Polydoros
4. Harish Chaandar Ravichandar

Citations in the corpus

• Benchmark for Skill Learning from Demonstration: Impact of User Experience, Task Complexity, and Start Configuration on Performance (2019, background): “Based on the choice of motion generation strategy, the majority of skill learning approaches can be further broadly categorized into four groups: time-invariant (stable) dynamical systems [2], [3], [13], time-dependent dynamical systems [7], [9], [10], time-dependent statistical methods [8], [14], [”
• Recent Advances in Robot Learning from Demonstration (2020, methodology): “Existing literature in LfD has explored a wide variety of choices for what is considered to be the state, such as end-effector position (81), velocity (70), orientation (82), force (66, 76), joint angles (75), and torques (79).”