Name
Title | ||
|---|---|---|
Robot Learning Physical Object Properties from Human Visual Cues: A novel approach to infer the fullness level in containers |
Abstract | ||
|---|---|---|
For collaborative tasks, involving handovers, humans are able to exploit visual, non-verbal cues, to infer physical object properties, like mass, to modulate their actions. In this paper, we investigate how the different levels of liquid inside a cup can be inferred from the observation of the movement of the person handling the cup. We model this mechanism from human experiments and incorporate it in an online human-to-robot handover. Finally, we provide a new dataset with human eye+head+hand motion data for human-to-human handovers and human pick-and-place of a cup with three levels of liquid: empty, half-full, and full of water. Our results show that it is possible to model (non-verbal) signals exchanged by humans during interaction and classify the level of water inside the cup being handed over. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/ICRA46639.2022.9811725 | IEEE International Conference on Robotics and Automation |
DocType | Volume | Issue |
Conference | 2022 | 1 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nuno Ferreira Duarte | 1 | 0 | 1.01 |
| Mirko Rakovic | 2 | 40 | 10.84 |
| Santos-Victor, J. | 3 | 1747 | 169.53 |