Learning CPG-based biped locomotion with a policy gradient method

By Takamitsu Matsubara, Jun Morimoto, Jun Nakanishi, Masa-aki Sato, Kenji Doya. 2005

32 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Cell Biology, Genetics, Reinforcement Learning, Robotics & Control

How researchers see this paper

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

In Biomedical Engineering

1. Nicolas Manfred Otto Heess
2. Timothy P. Lillicrap
3. Yuval Tassa
4. Sergey Levine
5. Tom Erez
6. Martin A. Riedmiller
7. Thomas Lampe
8. Roland Hafner

In Cell Biology

1. Nicolas Manfred Otto Heess

In Reinforcement Learning

1. Sergey Levine
2. Pieter Abbeel
3. Nicolas Manfred Otto Heess
4. Timothy P. Lillicrap
5. Shixiang Shane Gu
6. Martin A. Riedmiller
7. Chelsea Finn
8. Ofir Nachum

In Robotics & Control

1. Nicolas Manfred Otto Heess
2. Sergey Levine
3. Martin A. Riedmiller
4. Timothy P. Lillicrap
5. Pieter Abbeel
6. Thomas Lampe
7. Yuval Tassa
8. Tom Erez

Citations in the corpus

• Data-efficient Deep Reinforcement Learning for Dexterous Manipulation (2017, background): “…have been routinely applied in robotics, in part because they straightforwardly handle continuous and high-dimensional action spaces [3] and applications include manipulation [26, 13, 25, 37, 18, 5, 39, 8], locomotion e.g. [16, 21], and a range of other challenges such as helicopter flight [1].”