NOIR 2.0: Neural Signal Operated Intelligent Robots for Everyday Activities

TL;DR

NOIR 2.0 introduces an advanced brain-robot interface enabling more efficient and personalized control of robots through EEG signals, significantly reducing task completion time and human effort in daily activities.

Contribution

It presents NOIR 2.0 with faster decoding algorithms and foundation model-based few-shot learning for improved accuracy and user adaptation.

Findings

Task completion time reduced by 46%

Human effort decreased by 65%

Effective adaptation with fewer demonstrations

Abstract

Neural Signal Operated Intelligent Robots (NOIR) system is a versatile brain-robot interface that allows humans to control robots for daily tasks using their brain signals. This interface utilizes electroencephalography (EEG) to translate human intentions regarding specific objects and desired actions directly into commands that robots can execute. We present NOIR 2.0, an enhanced version of NOIR. NOIR 2.0 includes faster and more accurate brain decoding algorithms, which reduce task completion time by 46%. NOIR 2.0 uses few-shot robot learning algorithms to adapt to individual users and predict their intentions. The new learning algorithms leverage foundation models for more sample-efficient learning and adaptation (15 demos vs. a single demo), significantly reducing overall human time by 65%.