NOIR: Neural Signal Operated Intelligent Robots for Everyday Activities

Anthony Norcia; Ayano Hiranaka; Chen Wang; Gabrael Levine; Jiajun Wu; Jin Jie Ryan Tan; Li Fei-Fei; Minjune Hwang; Ruohan Gao; Ruohan Zhang

NOIR: Neural Signal Operated Intelligent Robots for Everyday Activities

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arxiv 2311.01454 v1 pith:BKSBVVU3 submitted 2023-11-02 cs.RO cs.AI

NOIR: Neural Signal Operated Intelligent Robots for Everyday Activities

Ruohan Zhang , Sharon Lee , Minjune Hwang , Ayano Hiranaka , Chen Wang , Wensi Ai , Jin Jie Ryan Tan , Shreya Gupta

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Yilun Hao Gabrael Levine Ruohan Gao Anthony Norcia Li Fei-Fei Jiajun Wu

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classification cs.RO cs.AI

keywords robotsactivitieseverydayhumansintelligentneuralnoirsystem

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We present Neural Signal Operated Intelligent Robots (NOIR), a general-purpose, intelligent brain-robot interface system that enables humans to command robots to perform everyday activities through brain signals. Through this interface, humans communicate their intended objects of interest and actions to the robots using electroencephalography (EEG). Our novel system demonstrates success in an expansive array of 20 challenging, everyday household activities, including cooking, cleaning, personal care, and entertainment. The effectiveness of the system is improved by its synergistic integration of robot learning algorithms, allowing for NOIR to adapt to individual users and predict their intentions. Our work enhances the way humans interact with robots, replacing traditional channels of interaction with direct, neural communication. Project website: https://noir-corl.github.io/.

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Cited by 1 Pith paper

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A hybrid visual-motor imagery EEG decoder controls a robot for grasping and placement at 40% and 63% accuracy respectively, yielding 21% end-to-end task success in cue-free online use.