Brain-Controlled Robots Excel with Shared Workload Support

The Role of Shared Autonomy in Assistive Robotics

A recent study published in Frontiers in Human Neuroscience highlights the potential benefits of shared autonomy in assistive robotics. This approach allows users to collaborate with robots, balancing automation with manual control. For individuals with severe motor impairments, such as those caused by amyotrophic lateral sclerosis (ALS), daily tasks like cooking or moving objects often require constant assistance from caregivers. While assistive robots could offer independence, many current systems are limited to simple, pre-programmed actions.

Brain-robot interfaces, which enable users to control robots using brain signals, present a promising alternative. However, these systems can be noisy, slow, and challenging to use without robotic assistance. Researchers at Araya Inc. in Tokyo, led by Hannah Douglas, aimed to address these challenges by developing a system that combines user input with robot autonomy.

Designing a Shared Control System

The team created a realistic virtual kitchen environment where two users could work alongside two mobile robots to complete tasks. Participants used a combination of brain signals via electroencephalography (EEG), muscle signals via electromyography (EMG), and eye-tracking to direct the robots’ actions. The system was designed to handle a wide range of daily-living tasks, from picking up dishes to moving pots and pans, while still giving users a meaningful sense of control.

To explore this, the researchers developed three different levels of autonomy for the robots:

Assisted Teleoperation: Users controlled nearly every step—selecting objects, choosing actions, and navigating the robot through the kitchen. At this level, the robot acts primarily as an executor of detailed instructions.
Shared Autonomy: Users still chose what they wanted the robot to do, but the robot handled navigation and some of the finer details. Users simply select a landmark with the eye tracker, and the robot moves autonomously, allowing them to focus on higher-level decisions.
Full Automation: Users selected a high-level goal, such as choosing a food item, and the robot completed the entire sequence on its own. In this condition, user input is minimal, focusing on high-level goal selection rather than stepwise control.

Testing the System

Thirty healthy adults participated in a controlled study to compare the three modes. Although the participants were not individuals with disabilities, the researchers used this group to test the system’s usability and performance before moving to clinical populations.

The results showed clear differences between the autonomy levels. Full Automation was the easiest for participants to use, requiring the least mental effort and completing tasks the fastest. Participants rated it highest for usability and lowest for workload. However, this convenience came at a cost: users felt less in control of the robot’s actions.

Assisted Teleoperation, by contrast, was the most demanding. Participants had to manage navigation, object selection, and action commands, leading to higher workload and lower performance. Many found it tiring and difficult to use.

Shared Autonomy offered a middle ground. It achieved a higher task success rate than Full Automation (80% compared to 66.7%) while preserving a stronger sense of agency. Maintaining independence and personal control is especially important in assistive technology, as it can empower individuals with severe motor impairments. The researchers found that Shared Autonomy was more reliable when EEG signals were noisy—a common issue in non-invasive brain-computer systems—because it utilized highly accurate eye-tracking to offset the potential for catastrophic robot errors.

Conclusion and Future Directions

“Therefore, while Full Automation is the optimal solution for efficiency, Shared Autonomy represents a valuable alternative for users who prioritize reliability and individuality,” Douglas and colleagues concluded.

The study has limitations. For example, all participants were healthy adults, meaning the results may not fully reflect the needs of people with ALS or other motor impairments.

The research, titled “Levels of shared autonomy in brain-robot interfaces: enabling multi-robot multi-human collaboration for activities of daily living,” was authored by Hannah Douglas, Marina Di Vincenzo, Rousslan Fernand Julien Dossa, Luca Nunziante, Shivakanth Sujit, and Kai Arulkumaran.