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By projecting into a 3-D workspace, robotic teleoperation using virtual reality allows for a more intuitive method of control for the operator than using a 2-D view from the robot's visual sensors. This paper investigates a setup that places the teleoperator in a virtual representation of the robot's environment and develops a deep learning based architecture modeling the correspondence between the operator's movements in the virtual space and joint angles for a humanoid robot using data collected from a series of demonstrations. We evaluate the correspondence model's performance in a pick-and - place teleoperation experiment.
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Telemanipulation via Virtual Reality Interfaces with Enhanced Environment Models
Extreme environments, such as search and rescue missions, defusing bombs, or exploring extraterrestrial planets, are unsafe environments for humans to be in. Robots enable humans to explore and interact in these environments through remote presence and teleoperation and virtual reality provides a medium to create immersive and easy-to-use teleoperation interfaces. However, current virtual reality interfaces are still very limited in their capabilities. In this work, we aim to advance robot teleoperation virtual reality interfaces by developing an environment reconstruction methodology capable of recognizing objects in a robot’s environment and rendering high fidelity models inside a virtual reality headset. We compare our proposed environment reconstruction method against traditional point cloud streaming by having operators plan waypoint trajectories to accomplish a pick-and-place task. Overall, our results show that users find our environment reconstruction method more usable and less cognitive work compared to raw point cloud streaming.
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- Award ID(s):
- 1944453
- PAR ID:
- 10334715
- Date Published:
- Journal Name:
- 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/IROS51168.2021.9636005
