Social Robots Learning On Their Own? New Study Skips Human Involvement In Early Testing

A new study from the University of Surrey and the University of Hamburg shows that humans are no longer the only drivers of training social robots for effective interaction.

Presented at the IEEE International Conference on Robotics and Automation (ICRA), the study unveils a new simulation approach that allows researchers to test social robots without human participants, enabling quicker and more scalable research.

The team used a humanoid robot to develop a scanpath prediction model that anticipates where a person might look in social situations. Tested on two publicly available datasets, the model showed that humanoid robots could replicate human-like eye movement patterns.

New Model Offers Human-like Focus Without Real-Time Supervision

According to, Dr. Di Fu, co-lead of the study and cognitive neuroscience lecturer at the University of Surrey, explained that their method allows researchers to assess if a robot focuses on the right elements, similar to a human, without live human oversight.

Howover, she highlighted that the model maintains its accuracy even in noisy and unpredictable settings, making it a valuable tool for practical uses in areas such as education, healthcare, and customer service.

Social robots are built to engage with humans through speech, gestures, and facial expressions, making them valuable in fields like education, healthcare, and customer support. Notable examples include Pepper, a retail assistant robot, and Paro, a therapeutic robot used with dementia patients.

The researchers aligned their model’s real-world performance with a simulated environment by projecting human gaze priority maps onto a screen, comparing the robot’s predicted focus of attention with actual human data.

This approach allowed them to assess social attention models in realistic conditions, reducing the need for extensive human-robot interaction studies early on.

Dr. Fu remarked, Replacing early human trials with robotic simulations marks a significant advancement in social robotics. It lets us test and enhance social interaction models, improving robots’ ability to understand and respond to humans. Next, we’ll apply this method to robot embodiment and assess its performance in complex social settings with various robot types.

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