Fast planning of well conditioned trajectories for model learning

By Cong Wang, Yu Zhao, Chung-Yen Lin, Masayoshi Tomizuka. 2014

13 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Statistical and Nonlinear Physics

How researchers see this paper

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

In Control and Systems Engineering

1. Sergey Levine
2. Pieter Abbeel
3. Chelsea Finn
4. Harish Chaandar Ravichandar
5. Vikash Kumar
6. Abhishek Gupta
7. Ashwin P. Dani
8. Jitendra Malik

In Statistical and Nonlinear Physics

1. Sergey Levine
2. Pieter Abbeel
3. Ashwin P. Dani
4. Harish Chaandar Ravichandar
5. Stefano Ermon
6. Chelsea Finn
7. Yang Song
8. Jonathan Ho

Citations in the corpus

• Model-based reinforcement learning with parametrized physical models and optimism-driven exploration (2015, background): “citation trajectories for model identification [5], [14], [25], [28], [31], [32].”
• Learning position and orientation dynamics from demonstrations via contraction analysis (2018, background): “Learning from demonstration (LfD) (Wang et al. 2014; Bil-lard and Matari´c 2001; Khansari-Zadeh and Billard 2011; Gribovskaya et al. 2010; Schaal 1999; Ijspeert et al. 2002) is a paradigm for training robots using demonstrations of a task.”