Name
Abstract | ||
|---|---|---|
In this paper, we consider an object recognition problem based on tactile information using a robot hand. The robot performs an exploratory action to the object to obtain the tactile information, however, poorly designed actions may not be sufficiently informative. In contrast, if we could collect sample data by sequentially performing informative actions, i.e., active learning, the required time would be drastically reduced. To this end, we propose a novel approach for active tactile object recognition. Our approach combines both an active learning scheme and a nonlinear dimensionality reduction method. We first extracts the object manifold, each coordinate of which represents an object, from tactile sensor data and action features using Gaussian Process Latent Variable Models. At the same time, a probabilistic model of the observed data related to the action and the object are learned. Then, with the learned model, optimally-informative exploratory actions can be computed sequentially, and performed to efficiently collect the data for recognition. We show experimental results that verify the effectiveness of our proposed method with synthetic data and a real robot. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/IROS.2014.6942622 | Intelligent Robots and Systems |
Keywords | Field | DocType |
Gaussian processes,learning (artificial intelligence),manipulators,object recognition,robot vision,Gaussian process latent variable models,active learning scheme,active tactile object recognition problem,nonlinear dimensionality reduction method,object manifold learning,probabilistic model,robot hand | Robot learning,Computer vision,Active learning,3D single-object recognition,Pattern recognition,Computer science,Object model,Synthetic data,Artificial intelligence,Nonlinear dimensionality reduction,Cognitive neuroscience of visual object recognition,Tactile sensor | Conference |
ISSN | Citations | PageRank |
2153-0858 | 12 | 0.59 |
References | Authors | |
15 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Daisuke Tanaka | 1 | 12 | 0.93 |
| Takamitsu Matsubara | 2 | 351 | 39.84 |
| Kentaro Ichien | 3 | 12 | 0.59 |
| Kenji Sugimoto | 4 | 30 | 10.35 |