NOIR 2.0: Neural Signal Operated Intelligent Robots for Everyday Activities
This work addresses the challenge of enabling humans, particularly those with disabilities, to interact with robots more efficiently for everyday activities, representing a significant incremental improvement over previous versions.
The paper tackles the problem of controlling robots for daily tasks using brain signals by presenting NOIR 2.0, an enhanced brain-robot interface that reduces task completion time by 46% and overall human time by 65% through faster decoding and few-shot learning algorithms.
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%.