Reinforcement learning of humanoid rhythmic walking parameters based on visual information
By M. Ogino, Yutaka Katoh, Masahiro Aono, Minoru Asada, K. Hosoda. 2004
38 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Biomedical 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
- Sergey Levine
- Chelsea Finn
- Pieter Abbeel
- Martin A. Riedmiller
- J. Urgen Schmidhuber
- Trevor Darrell
- Sascha Lange
- Jan Peters
In Biomedical Engineering
- Jie Tan
- Sergey Levine
- Tingnan Zhang
- Atil Iscen
- Dieter Fox
- Erwin Coumans
- Vincent Vanhoucke
- Martin A. Riedmiller
Citations in the corpus
- Sim-to-Real: Learning Agile Locomotion For Quadruped Robots (2018, background): “Much research has applied reinforcement learning to locomotion tasks [2, 47, 11, 22, 31].”
- Phase-Parametric Policies for Reinforcement Learning in Cyclic Environments (2018, background): “Cyclic Environments Shibuya and Yasunobu (2014) train agents for environments with cyclic time dependent rewards by using sinusoidal action-value functions in phasor representation.”
- Reinforcement learning for robot soccer (2009, methodology): “1 Scene from a game in the RoboCup middle size league The article is organized as follows: first, we will give a description of the batch RL framework and in particular describe three variants that have been proven useful for the application to challenging RL problems.”
- Reinforcement learning for sensing strategies (2004, background): “Furthermore, the system is highly non-linear and it is not clear how the different uncertainties affect the outcome of the actions, for example, of grabbing the ball or kicking it toward the goal.”