General In-Hand Object Rotation with Vision and Touch
Research output: Contribution to journal › Conference article › Contributed › peer-review
Contributors
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
| Original language | English |
|---|---|
| Journal | Proceedings of Machine Learning Research |
| Volume | 229 |
| Publication status | Published - 2023 |
| Peer-reviewed | Yes |
Conference
| Title | 7th Conference on Robot Learning |
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| Abbreviated title | CoRL 2023 |
| Conference number | 7 |
| Duration | 6 - 9 November 2023 |
| Website | |
| Degree of recognition | International event |
| Location | Starling Hotel |
| City | Atlanta |
| Country | United States of America |
External IDs
| ORCID | /0000-0001-9430-8433/work/158768044 |
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Keywords
ASJC Scopus subject areas
Keywords
- In-Hand Object Rotation, Reinforcement Learning, Sim-to-Real, Tactile Sensing, Transformer, Visuotactile Manipulation