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A new framework that simplifies imitation learning in robotics

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A diagram showing two halves of the researcher’s method. There is expression learning on the left side and behavioral imitation through the nearest neighbor on the right side. Credit: Pari et al.

Over the past few decades, computer scientists have sought to train robots to tackle a variety of tasks, such as household chores and manufacturing processes. One of the most famous strategies used to train robots with manual tasks is imitation learning.

As the name implies, imitation learning involves teaching robots how to do something with human demonstrations. In some studies, this training strategy has achieved very promising results, but often requires a large annotated dataset containing hundreds of videos in which humans complete a particular task.

Researchers at New York University recently developed VINN, an alternative imitation learning framework that does not necessarily require large training datasets. This new approach, presented in a pre-published paper on arXiv, works by separating two different aspects of imitation learning: the visual representation of tasks and the learning of related actions.

“I was interested in ways to simplify imitation learning,” Jyo Pari, one of the researchers who conducted the research, told TechXplore. “Imitation learning requires two basic components: one is to learn something related to the scene, and the other is a way to perform tasks using related features. These components that were combined into one system need to be separated and understood. The role and importance of each. “

Most existing imitation learning methods combine expression and behavioral learning into a single system. Meanwhile, the new method created by Pari and his colleagues focuses on feature learning. This is the process by which AI agents and robots learn to identify task-related features in the scene.

“We have adopted existing methods for self-monitoring expression learning, a popular area of ​​the vision community,” explained Paris. “These methods allow us to retrieve a collection of unlabeled images and extract related features. Applying these methods to imitation can identify the image that most closely resembles the image the robot is currently looking at. So it’s effective. Therefore, you can have the robot copy the action from a similar demo image. “

Using the new imitation learning strategy they developed, Paris and his colleagues were able to improve the performance of the visual imitation model in a simulated environment. He also tested the approach with a real robot and saw a similar demo image to efficiently teach him how to open the door.

“We feel that our work is the foundation of our future work, where we can use expression learning to enhance our imitation learning models,” says Paris. “But even if our method can perform a simple nearest neighbor task, it has some drawbacks.”

In the future, new frameworks may help simplify imitation learning processes in robotics and facilitate their large-scale implementation. So far, Paris and his colleagues have only used their strategy to train robots in simple tasks. Therefore, in the next study, we plan to explore strategies that may allow us to implement it in more complex tasks.

“It’s interesting to understand how to take advantage of the robustness of the nearest neighbors in more complex tasks with the power of parametric models,” Pari added. “We are currently working on scaling up VINN to perform not just one task, but several different tasks.”

Imitation learning approach to train robots without the need for real human demonstrations More information: Jyothish Pari, Nur Muhammad Shafiullah, Sridhar Pandian Arunachalam, Lerrel Pinto, amazing effects of expression learning for visual imitation. arXiv: 2112.01511v2 [cs.RO], Arxiv.org / abs / 2112.01511

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