Name
Title | ||
|---|---|---|
Data-Efficient and Safe Learning for Humanoid Locomotion Aided by a Dynamic Balancing Model. |
Abstract | ||
|---|---|---|
In this letter, we formulate a novel Markov Decision Process (MDP) for safe and data-efficient learning for humanoid locomotion aided by a dynamic balancing model. In our previous studies of biped locomotion, we relied on a low-dimensional robot model, commonly used in high-level Walking Pattern Generators (WPGs). However, a low-level feedback controller cannot precisely track desired footstep loc... |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/LRA.2020.2990743 | IEEE Robotics and Automation Letters |
Keywords | DocType | Volume |
feedback,humanoid robots,learning (artificial intelligence),legged locomotion,Markov processes,neurocontrollers,robot dynamics | Journal | 5 |
Issue | ISSN | Citations |
3 | 2377-3766 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Junhyeok Ahn | 1 | 2 | 3.42 |
| Jaemin Lee | 2 | 0 | 2.70 |
| Luis Sentis | 3 | 574 | 59.74 |