Abstract
Recently, the development of deep learning has enabled robots to grasp objects more reliably than ever. Given this fact, there is an increasing demand for helper robots or home robots. To make these robots real, robots need to understand not only how to grasp objects but also their functions. We propose a new representation for the functions of objects, task-oriented function, which is based on operational task input. This representation makes it possible to describe a variety of ways to use an object. We also propose a new dataset for task-oriented function and a network to detect it. This model reached 79.7% mean IOU in our dataset.
| Original language | English |
|---|---|
| Pages (from-to) | 1136-1142 |
| Number of pages | 7 |
| Journal | Seimitsu Kogaku Kaishi/Journal of the Japan Society for Precision Engineering |
| Volume | 85 |
| Issue number | 12 |
| DOIs | |
| Publication status | Published - 2019 |
Keywords
- Affordance
- Deep learning
- Robotics
- Segmentation
- Task-oriented
ASJC Scopus subject areas
- Mechanical Engineering