Sunday, May 19, 2024

Robots Intuitively Learn To Perform Independently!

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Researchers have developed a way to make robots more intelligent with the help of self supervised learning frameworks and even teaching them psychology.

The self-supervised learning framework Columbia Engineers call DextAIRity learns to effectively perform a target task through a sequence of grasping or air-based blowing actions. Using visual feedback, the system uses a closed-loop formulation that continuously adjusts its blowing direction. Credit: Zhenjia Xu/Columbia Engineering

Slowly but surely, robots are improving the quality of our lives by augmenting our abilities and enhancing our safety and health. Robots can now be seen in almost every other place like a restaurant, grocery stores, malls, hospitals etc. Existing robots are becoming very efficient with simple tasks, but for performing more complex tasks they will require more development in mobility and intelligence.

Columbia Engineering and Toyota Research Institute computer scientists are looking into psychology, physics, and geometry to create algorithms so that robots can adapt to their surroundings and learn how to do things independently. Object permanence, a well-known concept in psychology that involves understanding that the existence of an object is separate from whether it is visible at any moment, is a longstanding challenge in computer vision as most applications in computer vision ignore occlusions entirely and tend to lose track of objects that become temporarily hidden from view.

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To tackle this issue, researchers taught neural networks the basic physical concepts that come naturally to adults and children. Similar to how a child learns physics by watching events unfold in their surroundings, the team created a machine that watches many videos to learn physical concepts. The key idea is to train the computer to anticipate what the scene would look like in the future. By training the machine to solve this task across many examples, the machine automatically creates an internal model of how objects physically move in typical environments.

Researchers investigated how humans performed tasks intuitively to make robots more self reliable. Instead of trying to account for every possible parameter, her team developed an algorithm that allows the robot to learn from doing, making it more generalizable and lessening the need for massive amounts of training data.

Researchers modified the Iterative Residual Policy (IRP) algorithm which showed sub-inch accuracy by demonstrating it’s strong generalizing capabilities. They developed a new approach to manipulating them by using actively blown air. They armed their robot with an air pump and it was able to quickly unfold a large piece of cloth or open a plastic bag. The self-supervised learning framework they call DextAIRity learns to effectively perform a target task through a sequence of grasping or air-based blowing actions. Using visual feedback, the system uses a closed-loop formulation that continuously adjusts its blowing direction.

 

Currently, robots can successfully maneuver through a structured environment with clearly defined areas and do one task simultaneously. However, a truly useful home robot should have various skills, be able to work in an unstructured environment, like a living room with toys on the floor, and handle different situations. These robots will also need to know how to identify a task and which subtasks must be done in what order. And then, they will need to know what to do next if they fail at a job and how to adapt to the next steps needed to accomplish their goal.


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