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Stanford student robots set to make homework easier

Chelsea Finn, a computer science professor at Stanford, described Mobile Aloha’s two main functions.  Finn explained that Mobile Eloha has a remote operating system that makes it easy to gather performance data for complex projects. This allows the robot to perform tasks such as cooking shrimp after data collection instead of performing manual programming step by step.

Finn went on to say that Mobile Aloha proves that robots can easily learn from data collected through remote operations to complete such complex tasks on their own.

Stanford students Zipeng Fu, co-leader of Project Mobile ALOHA, explains the challenges of the robot's 3-month development process. He describes the technical challenges as twofold, hardware challenges and software challenges.

In the past, researchers have used expensive hardware, such as robotic drones purchased from manufacturers, for experiments. The Mobile ALOHA team fought this common practice by supplementing their hardware, creating a better and less expensive teleoperating system.

Another software challenge that presented itself was the need for step-by-step, explicit gestures to make robots perform everyday tasks more efficiently Team Mobile ALOHA decided to take a different approach than the traditional manual process.

“It is a data-driven AI approach by teaching human performance data to robots (i.e. simulation learning). We have shown that simulation learning combined with co-training techniques is effective in teaching robots new skills.