Levels of shared autonomy in brain-robot interfaces: enabling multi-robot multi-human collaboration for activities of daily living - Douglas, Hannah; Di Vincenzo, Marina; Dossa, Rousslan Fernand Julien; Nunziante, Luca; Sujit, Shivakanth; Arulkumaran, Kai (Frontiers in Human Neuroscience, 2026)
Individuals with ALS and other severe motor impairments often rely on caregivers for daily tasks, which limits their independence and sense of control. Brain-robot interfaces (BRIs) have the potential to restore autonomy, but many existing systems are task-specific and highly automated, which reduces the users' sense of empowerment and limits opportunities to exercise autonomy. In particular, shared autonomy approaches hold promise for overcoming current BRI limitations, by balancing user control with increased robot capabilities. In this work, we introduce a collaborative BRI that integrates non-invasive EEG, EMG, and eye tracking to enable multi-user, multi-robot interaction in a shared kitchen environment with mobile manipulators. Our system modulates assistance through three levels of autonomy—Assisted Teleoperation, Shared Autonomy, and Full Automation—allowing users to retain meaningful control over task execution while reducing effort for routine operations. We conducted a controlled user study comparing autonomy conditions, evaluating performance, workload, ease of use, and agency. Our results show that, while Full Automation was generally preferred by users due to lower workload and higher usability, Shared Autonomy provided higher reliability and preserved user agency, especially in the presence of noisy EEG decoding. Although there was significant individual variability in EEG decoding performance, our post-hoc analysis revealed the potential benefits of customizing pipelines for each user. Finally, we note that our findings are specific to the multi-modal configuration tested and should not be interpreted as a universal claim about the superiority of any autonomy level, and, furthermore, our user study was limited by the use of healthy adults rather than target population (e.g., individuals with ALS), gender imbalance, and a relatively small sample size, which may affect generalizability. Project website: https://coopopen.github.io/.