Name
Abstract | ||
|---|---|---|
In this paper, we attack the problem of learning a predictive model of a depth camera and manipulator directly from raw execution traces. While the problem of learning manipulator models from visual and proprioceptive data has been addressed before, existing techniques often rely on assumptions about the structure of the robot or tracked features in observation space. We make no such assumptions. Instead, we formulate the problem as that of learning a high-dimensional controlled stochastic process. We leverage recent work on nonparametric predictive state representations to learn a generative model of the depth camera and robotic arm from sequences of uninterpreted actions and observations. We perform several experiments in which we demonstrate that our learned model can accurately predict future depth camera observations in response to sequences of motor commands. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/ICRA.2014.6907443 | ICRA |
Keywords | Field | DocType |
nonparametric predictive state representations,stochastic processes,predictive model learning,depth camera,learning (artificial intelligence),observation space,motor commands,execution trace,high-dimensional controlled stochastic process,robotic arm,cameras,manipulator model learning,manipulators | Computer science,Manipulator,Control engineering | Conference |
Volume | Issue | ISSN |
2014 | 1 | 1050-4729 |
Citations | PageRank | References |
3 | 0.41 | 10 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Byron Boots | 1 | 471 | 50.73 |
| Arunkumar Byravan | 2 | 78 | 5.56 |
| Dieter Fox | 3 | 12306 | 1289.74 |