Abstract
Brain-Computer Interfaces are promising technologies that can improve Human-Robot Interaction, especially for disabled and impaired individuals. Non-invasive BCI’s, which are very desirable from a medical and therapeutic perspective, are only able to deliver noisy, low-bandwidth signals, making their use in complex tasks difficult. To this end, we present a shared control online grasp planning framework using an advanced EEG-based interface. Unlike commonly used paradigms, the EEG interface we incorporate allows online generation of a flexible number of options. This online planning framework allows the user to direct the planner towards grasps that reflect their intent for using the grasped object by successively selecting grasps that approach the desired approach direction of the hand. The planner divides the grasping task into phases, and generates images that reflect the choices that the planner can make at each phase. The EEG interface is used to recognize the user’s preference among a set of options presented by the planner. The EEG signal classifier is fast and simple to train, and the system as a whole requires almost no learning on the part of the subject. Three subjects were able to successfully use the system to grasp and pick up a number of objects in a cluttered scene.
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Acknowledgements
The authors would like to gratefully acknowledge the help of Dr. Paul Sajda and Dr. David Jangraw in this work. This work was supported by NSF Grant IIS-1208153.
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Ying, R., Weisz, J., Allen, P.K. (2018). Grasping with Your Brain: A Brain-Computer Interface for Fast Grasp Selection. In: Bicchi, A., Burgard, W. (eds) Robotics Research. Springer Proceedings in Advanced Robotics, vol 2. Springer, Cham. https://doi.org/10.1007/978-3-319-51532-8_20
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