Professor Chelsea Finn and researchers have created a teleoperation structure that allows the Mobile ALOHA robot to learn many complex activities, impressively fast.

“We’re providing a general framework that allows people to show robots how to do a task and then allows robots to learn from what they were shown,” states Chelsea. She advises the Mobile ALOHA team, which is led by computer science graduate students Zipeng Fu and Tony Z. Zhao.

Some general-use robots are programmed step-by-step and others, including Mobile ALOHA, learn through demonstration. But the Mobile ALOHA researchers took imitation learning a step further: The researchers strap themselves into a teleoperation system directly behind the robot’s arms and puppeteer the robot through the desired actions.

Once Mobile ALOHA is operated through a task in a set environment about 50 times, powerful imitation learning algorithms help it make the leap to doing that task independently. (These algorithms are similar to the large language models behind popular chatbots, but for physical action instead of words.)

So far, the researchers have taught Mobile ALOHA to autonomously put away a cooking pot in a cabinet, call an elevator, push in chairs, sauté shrimp, clean up a wine spill, and give high-fives. Future versions of the robot may be smaller with greater freedom of movement, and be easier for non-experts to operate.

“The robot isn’t just a cool machine, we also wanted it to be fun and appeal to what people think a future robot should look like,” said Zhao.

“Helping robots is a very promising future of the field where we – as AI researchers and roboticists – can make a positive impact in society,” said Fu.

• Excerpted from “Meet the robot that can sauté shrimp,” Stanford Report, April 30, 2024.
• Mobile ALOHA