Stanford
Assembly Line
A simpler method for learning to control a robot
Researchers from MIT and Stanford University have devised a new machine-learning approach that could be used to control a robot, such as a drone or autonomous vehicle, more effectively and efficiently in dynamic environments where conditions can change rapidly.
The researchers’ approach incorporates certain structure from control theory into the process for learning a model in such a way that leads to an effective method of controlling complex dynamics, such as those caused by impacts of wind on the trajectory of a flying vehicle. With this structure, they can extract a controller directly from the dynamics model, rather than using data to learn an entirely separate model for the controller.
The researchers also found that their method was data-efficient, which means it achieved high performance even with few data. For instance, it could effectively model a highly dynamic rotor-driven vehicle using only 100 data points. Methods that used multiple learned components saw their performance drop much faster with smaller datasets.
Stanford researchers propose AI that figures out how to use real-world objects
One longstanding goal of AI research is to allow robots to meaningfully interact with real-world environments. In a recent paper, researchers at Stanford and Facebook took a step toward this by extracting information related to actions like pushing or pulling objects with movable parts and using it to train an AI model. For example, given a drawer, their model can predict that applying a pulling force on the handle would open the drawer.