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In this paper, we design and evaluate a novel form of visually-simulated haptic feedback cue for communicating weight in robot teleoperation. We propose that a visuo-proprioceptive cue results from inconsistencies created between the user's visual and proprioceptive senses when the robot's movement differs from the movement of the user's input. In a user study where participants teleoperate a six-DoF robot arm, we demonstrate the feasibility of using such a cue for communicating weight in four telemanipulation tasks to enhance user experience and task performance.
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Deep Correspondence Learning for Effective Robotic Teleoperation using Virtual Reality
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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- Award ID(s):
- 1652530
- PAR ID:
- 10179938
- Date Published:
- Journal Name:
- 2019 IEEE-RAS 19th International Conference on Humanoid Robots (Humanoids)
- Page Range / eLocation ID:
- 477 to 483
- 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/Humanoids43949.2019.9035031
