A new machine-learning strategy has been developed by researchers from MIT and Stanford University that might be used to more successfully and efficiently drive a robot, such as a drone or autonomous vehicle, in dynamic environments where conditions can change quickly.This method might enable a drone to closely follow a downhill skier despite being buffeted by high winds, a robotic free-flyer haul various items in space, and an automated vehicle to learn to correct for slick road conditions to avoid entering a skid.
The researchers’ method incorporates a specific structure from control theory into the learning process of a model in a way that results in an efficient way to regulate complicated dynamics, including those brought on by wind impacts on the trajectory of a flying object. This structure can be viewed as a clue that can help direct system control, to use one way of thinking about it. According to Navid Azizan, the Esther and Harold E. Edgerton Assistant Professor in the MIT Department of Mechanical Engineering and the Institute for Data, Systems, and Society (IDSS), who is also a member of the Laboratory for Information and Decision Systems (LIDS), “the focus of our work is to learn intrinsic structure in the dynamics of the system that can be leveraged to design more effective, stabilizing controllers.”
We are able to organically design controllers that perform considerably more successfully in the actual world by combining learning the system’s dynamics and these special control-oriented structures from data.The researchers’ method uses this structure in a learnt model to instantly extract an efficient controller from the model, unlike other machine-learning techniques that call for a controller to be generated or taught independently with additional steps. This structure also allows their method to learn an efficient controller with less input than other methods. This may enable their learning-based control system to operate better and more quickly in dynamic conditions.
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