Name
Abstract | ||
|---|---|---|
Understanding and representing objects and their function is a challenging task. Objects we manipulate in our daily activities can be described and categorized in various ways according to their properties or affordances, depending also on our perception of those. In this work, we are interested in representing the knowledge acquired through interaction with objects, describing these in terms of action-effect relations, i.e. sensorimotor contingencies, rather than static shape or appearance representations. We demonstrate how a robot learns sensorimotor contingencies through pushing using a probabilistic model. We show how functional categories can be discovered and how entropy-based action selection can improve object classification. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/IROS.2013.6696752 | 2013 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS) |
Keywords | Field | DocType |
probability,image classification,robotics | Object detection,Computer vision,Computer science,Artificial intelligence,Contextual image classification,Action selection,Robot,Affordance,Perception,Robotics,Knowledge acquisition | Conference |
ISSN | Citations | PageRank |
2153-0858 | 6 | 0.49 |
References | Authors | |
11 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Virgile Hogman | 1 | 30 | 1.31 |
| Mårten Björkman | 2 | 202 | 13.90 |
| Danica Kragic | 3 | 2070 | 142.17 |