Name
Abstract | ||
|---|---|---|
Human-robot object handovers have been an actively studied area of robotics over the past decade; however, very few techniques and systems have addressed the challenge of handing over diverse objects with arbitrary appearance, size, shape, and deformability. In this paper, we present a vision-based system that enables reactive human-to-robot handovers of unknown objects. Our approach combines closed-loop motion planning with real-time, temporally consistent grasp generation to ensure reactivity and motion smoothness. Our system is robust to different object positions and orientations, and can grasp both rigid and non-rigid objects. We demonstrate the generalizability, usability, and robustness of our approach on a novel benchmark set of 26 diverse household objects, a user study with six participants handing over a subset of 15 objects, and a systematic evaluation examining different ways of handing objects. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/ICRA48506.2021.9561170 | 2021 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA 2021) |
DocType | Volume | Issue |
Conference | 2021 | 1 |
ISSN | Citations | PageRank |
1050-4729 | 0 | 0.34 |
References | Authors | |
4 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wei Yang | 1 | 0 | 1.01 |
| Chris Paxton | 2 | 46 | 13.91 |
| Arsalan Mousavian | 3 | 11 | 5.27 |
| Yu-Wei Chao | 4 | 241 | 9.87 |
| Maya Cakmak | 5 | 882 | 58.40 |
| Dieter Fox | 6 | 12306 | 1289.74 |