Robots learn household tasks by watching humans



Shikhar Bahl opened the refrigerator door while the robot observed. It observed his actions, the way the door swung open, the position of the refrigerator, and other details before evaluating the information and getting ready to imitate what Bahl had done.

It first failed, occasionally entirely missing the handle, gripping it at the wrong place, or dragging it in the wrong direction. However, after some practice, the robot was successful in opening the door.

"Imitation is a terrific approach to learn," said Bahl, a Ph.D. candidate at Carnegie Mellon University's Robotics Institute (RI). Although it's still a challenge, this effort significantly advances the possibility of allowing robots to learn by directly observing people.

In-the-Wild Human Imitating Robot Learning, or WHIRL for short, is a novel technique that Bahl created with Deepak Pathak and Abhinav Gupta, both professors at the RI. The one-shot visual imitation algorithm WHIRL is effective. Robots are ideally adapted to learning home duties since they can immediately learn from recordings of human-human interaction and generalize that information to new activities. In their houses, people continually carry out a variety of duties. A robot may monitor those tasks with WHIRL and collect the video data it requires to finally figure out how to do the work itself.

The team modified an off-the-shelf robot by adding a camera and their software, and the robot quickly learned to perform more than 20 different tasks, such as opening and closing cabinets, drawers, and appliances as well as placing lids on pots, pushing in chairs, and even removing trash bags from bins. Each time, the robot saw a person perform the activity once before starting to practice and learn how to conduct it independently. During this month's Robotics: Science and Systems conference in New York, the researchers presented their findings.

Pathak, an assistant professor at the RI and a team member, stated that "this study gives a means to introduce robots into the household." This technology enables us to deploy the robots and have them learn how to complete tasks, all the while adapting to their environments and improving solely by observation. "Instead of waiting for robots to be programmed or trained to successfully complete different tasks before deploying them into people's homes," says the researcher.

The majority of current techniques for teaching a robot a task rely on reinforcement learning or imitation. In imitation learning, a robot is taught to do a task by being manually operated by humans. Before the robot learns, this technique must be repeated numerous times for a single job. In reinforcement learning, the robot is frequently instructed to adjust its training to the actual environment after being taught on millions of instances in simulation.

When teaching a robot a single task in a structured environment, both learning models perform well, but they are challenging to scale and implement. Any video of a person performing a job can be used by WHIRL to learn. It is readily expandable, not restricted to a single activity, and capable of functioning in actual household contexts. Even now, the team is working on a WHIRL variant that learns from watching footage of people interacting on YouTube and Flickr.

The work was made feasible by advancements in computer vision. Computers can now comprehend and simulate movement in three dimensions using models trained on internet data. The team trained WHIRL by using these models to comprehend human movement.

A robot can carry out activities in its surroundings by using WHIRL. The seats, garbage bag, doors, drawers, lids, and other objects were not altered or moved to accommodate the robot. The robot failed its first few efforts at a task, but after a few successes, it soon grasped how to complete it and perfected it. While a robot might not do the work using human-like gestures, that is not the intended outcome. Robots and people move differently and have separate components. The fact that the outcome is the same is what counts. A door is unlocked. The switch has been disabled. The water supply is running.

According to Pathak, for robotics to be scaled in the real world, the data must be dependable and steady, and the robots must improve independently as they practice in their surroundings                                           

Carnegie Mellon University

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