Thursday, June 13, 2024

Robots Predict Human Intention For Providing Better Assistance

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Researchers have developed a system that can make robots proactive by learning and therefore understanding human preferences.

PhD student Heramb Nemlekar (left) and assistant professor Stefanos Nikolaidis aim to teach robots how to predict human preferences in assembly tasks. Credit: Keith Wang

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.

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Therefore, instead of “showing” the robot their preferences in a complex task, they created a small assembly task (called a “canonical” task) that people can easily and quickly perform. In this case, putting together parts of a simple model airplane, such as the wings, tail and propeller. The robot “watched” the human complete the task using a camera placed directly above the assembly area, looking down. To detect the parts operated by the human, the system used AprilTags, similar to QR codes, attached to the parts.
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.

This technology could also be useful in industrial settings where workers are tasked with assembling products on a mass scale, saving time and reducing the risk of injury or accidents. Additionally, it could help persons with disabilities or limited mobility to more easily assemble products and maintain independence.

Reference : Transfer Learning of Human Preferences for Proactive Robot Assistance in Assembly Tasks, … ce_in_Assembly_Tasks ,


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