Versatile Demonstration Interface: Toward More Flexible Robot Demonstration Collection
Fuente:
arXiv
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909536541999104 |
|---|---|
| author | Hagenow, Michael Kontogiorgos, Dimosthenis Wang, Yanwei Shah, Julie |
| author_facet | Hagenow, Michael Kontogiorgos, Dimosthenis Wang, Yanwei Shah, Julie |
| contents | Previous methods for Learning from Demonstration leverage several approaches for a human to teach motions to a robot, including teleoperation, kinesthetic teaching, and natural demonstrations. However, little previous work has explored more general interfaces that allow for multiple demonstration types. Given the varied preferences of human demonstrators and task characteristics, a flexible tool that enables multiple demonstration types could be crucial for broader robot skill training. In this work, we propose Versatile Demonstration Interface (VDI), an attachment for collaborative robots that simplifies the collection of three common types of demonstrations. Designed for flexible deployment in industrial settings, our tool requires no additional instrumentation of the environment. Our prototype interface captures human demonstrations through a combination of vision, force sensing, and state tracking (e.g., through the robot proprioception or AprilTag tracking). Through a user study where we deployed our prototype VDI at a local manufacturing innovation center with manufacturing experts, we demonstrated VDI in representative industrial tasks. Interactions from our study highlight the practical value of VDI's varied demonstration types, expose a range of industrial use cases for VDI, and provide insights for future tool design. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_19141 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Versatile Demonstration Interface: Toward More Flexible Robot Demonstration Collection Hagenow, Michael Kontogiorgos, Dimosthenis Wang, Yanwei Shah, Julie Robotics Previous methods for Learning from Demonstration leverage several approaches for a human to teach motions to a robot, including teleoperation, kinesthetic teaching, and natural demonstrations. However, little previous work has explored more general interfaces that allow for multiple demonstration types. Given the varied preferences of human demonstrators and task characteristics, a flexible tool that enables multiple demonstration types could be crucial for broader robot skill training. In this work, we propose Versatile Demonstration Interface (VDI), an attachment for collaborative robots that simplifies the collection of three common types of demonstrations. Designed for flexible deployment in industrial settings, our tool requires no additional instrumentation of the environment. Our prototype interface captures human demonstrations through a combination of vision, force sensing, and state tracking (e.g., through the robot proprioception or AprilTag tracking). Through a user study where we deployed our prototype VDI at a local manufacturing innovation center with manufacturing experts, we demonstrated VDI in representative industrial tasks. Interactions from our study highlight the practical value of VDI's varied demonstration types, expose a range of industrial use cases for VDI, and provide insights for future tool design. |
| title | Versatile Demonstration Interface: Toward More Flexible Robot Demonstration Collection |
| topic | Robotics |
| url | https://arxiv.org/abs/2410.19141 |