Researchers at Cornell have developed a new robotic framework powered by artificial intelligence – called RHyME (Retrieval for Hybrid Imitation under Mismatched Execution) – that allows robots to learn tasks by watching a single how-to video. RHyME could fast-track the development and deployment of robotic systems by significantly reducing the time, energy and money needed to train them, the researchers said.
“One of the annoying things about working with robots is collecting so much data on the robot doing different tasks,” said Kushal Kedia, a doctoral student in the field of computer science and lead author of a corresponding paper on RHyME – a groundbreaking development that promises to revolutionize robotics. “That’s not how humans do tasks. We look at other people as inspiration.”
The team’s work centers around the core principle of imitation learning, where robots learn by observing human demonstrations. This approach drastically reduces the need for extensive, labeled datasets, addressing a major bottleneck in traditional robot training methods. The RHyME system demonstrates remarkable efficiency, requiring only 30 minutes of robot data to achieve significant task success – a substantial improvement over previous techniques.
Kedia will present the paper, One-Shot Imitation under Mismatched Execution, in May at the Institute of Electrical and Electronics Engineers’ International Conference on Robotics and Automation, in Atlanta. This conference highlights the cutting edge of robotics research.
home robot assistants are still a long way off – it is a very difficult task to train robots to deal with all the potential scenarios that they could encounter in the real world. To get robots up to speed, researchers like Kedia are training them with what amounts to how-to videos – human demonstrations of various tasks in a lab setting. The hope with this approach, a branch of machine learning called “imitation learning,” is that robots will learn a sequence of tasks faster and be able to adapt to real-world environments. RHyME represents a pivotal shift in robotic learning paradigms.
“Our work is like translating French to English – we’re translating any given task from human to robot,” said senior author Sanjiban Choudhury, assistant professor of computer science in the Cornell Ann S. Bowers College of Computing and Information Science. “It’s a direct translation that addresses the inherent mismatches between human and robotic movements.”
This translation task still faces a broader challenge, however: Humans move too fluidly for a robot to track and mimic, and training robots with video requires gobs of it. Further, video demonstrations – of, say, picking up a napkin or stacking dinner plates – must be performed slowly and flawlessly, since any mismatch in actions between the video and the robot has historically spelled doom for robot learning, the researchers said. RHyME’s scalable approach mitigates this issue.
RHyME is the team’s answer – a scalable approach that makes robots less finicky and more adaptive. It trains a robotic system to store previous examples in its memory bank and connect the dots when performing tasks it has viewed only once by drawing on videos it has seen. For example, a RHyME-equipped robot shown a video of a human fetching a mug from the counter and placing it in a nearby sink will comb its bank of videos and draw inspiration from similar actions – like grasping a cup and lowering a utensil. This exemplifies the power of retrieval-based learning.
RHyME paves the way for robots to learn multiple-step sequences while significantly lowering the amount of robot data needed for training, the researchers said. They claim that RHyME requires just 30 minutes of robot data; in a lab setting, robots trained using the system achieved a more than 50% increase in task success compared to previous methods. The implications are enormous for industries reliant on robotics.
“This work is a departure from how robots are programmed – it’s about learning from demonstration rather than painstakingly programming every detail,” Kedia added.” ,
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