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

Keywords: Brain-Robot Interface, Human-Robot Interaction

TL;DR: We present a general-purpose, intelligent brain-robot interface system that enables humans to command robots to perform everyday activities through brain signals.

Abstract: 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.

Student First Author: no

Instructions: I have read the instructions for authors (https://corl2023.org/instructions-for-authors/)

Website: https://noir-corl.github.io/

Community Implementations: [ 1 code implementation](https://www.catalyzex.com/paper/noir-neural-signal-operated-intelligent/code)