Name
Abstract | ||
|---|---|---|
Researchers at IHMC and Honda Research Institute are developing techniques for learning capture points for bipedal push recovery. A capture point is a point on the ground where a biped can step to in order to stop. Humans are very adept at stepping to capture points, while most bipedal robots cannot recover from significant pushes. To calculate approximate capture point locations, we use the linear inverted pendulum model introduced by Kajita and Tani. For a point mass biped walking at a constant height, this model exactly predicts the capture point. However, for a distributed mass biped, it is only an approximation. In order to better predict capture points, we learn a correction function to the linear inverted pendulum model. We used two learning methods, one online and one offline, to improve capture point prediction. In the offline learning method, the robot is pushed multiple times with a given force magnitude and direction. In the online learning technique, we use a radial basis function to represent the learned offsets from the capture point predicted by the linear inverted pendulum model. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/ROBOT.2008.4543460 | ICRA |
Keywords | Field | DocType |
function approximation,learning (artificial intelligence),legged locomotion,linear systems,nonlinear control systems,bipedal push recovery,bipedal robot,capture point location approximation,capture point prediction,correction function,distributed mass biped walking,linear inverted pendulum model,offline learning method,online learning method,radial basis function | Online learning,Offline learning,Inverted pendulum,Radial basis function,Function approximation,Linear system,Control theory,Control engineering,Point particle,Engineering,Robot | Conference |
Volume | Issue | ISSN |
2008 | 1 | 1050-4729 |
Citations | PageRank | References |
8 | 1.04 | 4 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| John R. Rebula | 1 | 65 | 4.55 |
| Fabian Canas | 2 | 13 | 1.58 |
| Jerry E. Pratt | 3 | 888 | 89.98 |
| Ambarish Goswami | 4 | 1345 | 119.12 |