Humans possess the unique ability to grasp the goals, desires, and beliefs of others, which is crucial for anticipating actions and collaborating effectively. This skill, often known as “theory of mind,” is innate to us but stays a challenge for robots. Nevertheless, if robots are to change into truly collaborative helpers in manufacturing and day by day life, they should learn these abilities as well.

In a latest paper, which was a finalist for the very best paper award on the ACM/IEEE International Conference on Human-Robot Interaction (HRI), computer science researchers from USC Viterbi aim to show robots to predict human preferences in assembly tasks. It will allow robots to in the future assist in various tasks, from constructing satellites to setting a table.

“When working with people, a robot must always guess what the person will do next,” said lead writer Heramb Nemlekar, a USC computer science PhD student supervised by Stefanos Nikolaidis, an assistant professor of computer science. “For instance, if the robot thinks the person will need a screwdriver to assemble the following part, it will probably get the screwdriver ahead of time in order that the person doesn’t need to wait. This fashion the robot may help people finish the assembly much faster.”

A Latest Approach to Predicting Human Actions

Predicting human actions will be difficult, as different people prefer to finish the identical task in various ways. Current techniques require people to exhibit how they would really like to perform the assembly, which will be time-consuming and counterproductive. To deal with this issue, the researchers discovered similarities in how individuals assemble different products and used this data to predict preferences.

As a substitute of requiring individuals to “show” the robot their preferences in a posh task, the researchers created a small assembly task (known as a “canonical” task) that may very well be quickly and simply performed. The robot would then “watch” the human complete the duty using a camera and utilize machine learning to learn the person’s preference based on their sequence of actions within the canonical task.

In a user study, the researchers’ system was in a position to predict human actions with around 82% accuracy. This approach not only saves effort and time but in addition helps construct trust between humans and robots. It may very well be useful in industrial settings, where employees assemble products on a big scale, in addition to for individuals with disabilities or limited mobility who require assistance in assembling products.

Towards a Way forward for Enhanced Human-Robot Collaboration

The researchers’ goal is just not to switch human employees but to enhance safety and productivity in human-robot hybrid factories by having robots perform non-value-added or ergonomically difficult tasks. Future research will give attention to developing a technique to mechanically design canonical tasks for several types of assembly tasks and evaluating the advantages of learning human preferences from short tasks and predicting actions in complex tasks in various contexts, reminiscent of personal assistance in homes.

“A robot that may quickly learn our preferences may help us prepare a meal, rearrange furniture, or do house repairs, having a big impact on our day by day lives,” said Nikolaidis.