Researchers have developed a system that can make robots proactive by learning and therefore understanding human preferences.
However hard we try to create a robot or a machine that can meet the human level of thought processing and behaviors, there will always be a thin line standing between the two. Humans have a way of understanding others’ goals, desires and beliefs, a crucial skill that allows us to anticipate people’s actions. Taking bread out of the toaster? You’ll need a plate. Sweeping up leaves? I’ll grab the green trash can. This skill, often referred to as “theory of mind,” comes easily to us as humans, but is still challenging for robots. But, if robots are to become truly collaborative helpers in manufacturing and in everyday life, they need to learn the same abilities.
Researchers from the University of Southern California have been working on enabling robots to predict human preferences in assembly tasks, so they can one day help out on everything from building a satellite to setting a table. Most of the current techniques require people to show the robot how they would like to perform the assembly, but this takes time and effort and can defeat the purpose. Researchers figured similarities in how an individual will assemble different products. For instance, if you start with the hardest part when building an Ikea sofa, you are likely to use the same tact when putting together a baby’s crib.
The system used machine learning to learn a person’s preference based on their sequence of actions in the canonical task. Test results showed that the system was able to predict the actions that humans will take with around 82% accuracy.
Reference : Transfer Learning of Human Preferences for Proactive Robot Assistance in Assembly Tasks, www.researchgate.net/publicati … ce_in_Assembly_Tasks , humanrobotinteraction.org/2023/