General In-Hand Object Rotation with Vision and Touch
Publikation: Beitrag in Fachzeitschrift › Konferenzartikel › Beigetragen › Begutachtung
Beitragende
Abstract
We introduce RotateIt, a system that enables fingertip-based object rotation along multiple axes by leveraging multimodal sensory inputs. Our system is trained in simulation, where it has access to ground-truth object shapes and physical properties. Then we distill it to operate on realistic yet noisy simulated visuotactile and proprioceptive sensory inputs. These multimodal inputs are fused via a visuotactile transformer, enabling online inference of object shapes and physical properties during deployment. We show significant performance improvements over prior methods and the importance of visual and tactile sensing.
Details
| Originalsprache | Englisch |
|---|---|
| Fachzeitschrift | Proceedings of Machine Learning Research |
| Jahrgang | 229 |
| Publikationsstatus | Veröffentlicht - 2023 |
| Peer-Review-Status | Ja |
Konferenz
| Titel | 7th Conference on Robot Learning |
|---|---|
| Kurztitel | CoRL 2023 |
| Veranstaltungsnummer | 7 |
| Dauer | 6 - 9 November 2023 |
| Webseite | |
| Bekanntheitsgrad | Internationale Veranstaltung |
| Ort | Starling Hotel |
| Stadt | Atlanta |
| Land | USA/Vereinigte Staaten |
Externe IDs
| ORCID | /0000-0001-9430-8433/work/158768044 |
|---|
Schlagworte
ASJC Scopus Sachgebiete
Schlagwörter
- In-Hand Object Rotation, Reinforcement Learning, Sim-to-Real, Tactile Sensing, Transformer, Visuotactile Manipulation