Name
Abstract | ||
|---|---|---|
We consider the problem of acquiring mechanical knowledge through visual cues to help robots use objects in new situations. In this work, we propose a novel deep learning approach that allows a robot to acquire mechanical knowledge from 3D point clouds. This presents two main challenges. The first challenge is that a robot needs to infer novel objects’ functions from its experience. Secondly, the robot should also need to know how to manipulate these novel objects. To solve these problems, we present a two-branch deep neural network. The first branch detects function parts from the point clouds while the second branch predicts offset poses. Fusing the results from these two branches, our approach can not only detect what functions the novel objects may have but also generate key object states which can be used to guide a robot to manipulate these objects. We show that even though most of the training samples are synthetic data, our model still learns useful features and outputs proper results. Finally, we evaluate our approach on a real robot to run a series of tasks. The experimental results show that our approach has the capability to transfer mechanical knowledge in new situations. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/IROS45743.2020.9341558 | IROS |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zijia Li | 1 | 4 | 2.25 |
| Kei Okada | 2 | 534 | 118.08 |
| Masayuki Inaba | 3 | 2186 | 410.27 |