“The hierarchy, including the use of this low-level controller, enables us to constrain the robot’s behavior so it is more well-behaved. This low-level controller is not a neural network and instead relies on a set of concise, physical equations that describe the robot’s motion. That neural network outputs a target trajectory, which the second controller uses to come up with torques for each of the robot’s 12 joints. The high-level controller is a neural network that “learns” from experience. The robot’s camera captures depth images of the upcoming terrain, which are fed to a high-level controller along with information about the state of the robot’s body (joint angles, body orientation, etc.). To develop their system, the researchers took the best elements from these robust, blind controllers and combined them with a separate module that handles vision in real-time. Systems that do incorporate vision usually rely on a “heightmap” of the terrain, which must be either preconstructed or generated on the fly, a process that is typically slow and prone to failure if the heightmap is incorrect. Vision is such a complex sensory input to process that these algorithms are unable to handle it efficiently. Many blind controllers - those that do not incorporate vision - are robust and effective but only enable robots to walk over continuous terrain. The use of two separate controllers working together makes this system especially innovative.Ī controller is an algorithm that will convert the robot’s state into a set of actions for it to follow. The work will be presented next month at the Conference on Robot Learning. Other co-authors include Kartik Paigwar, a graduate student at Arizona State University and Donghyun Kim, an assistant professor at the University of Massachusetts at Amherst. and Renee Finn Career Development Assistant Professor in the Department of Electrical Engineering and Computer Science Professor Sangbae Kim in the Department of Mechanical Engineering at MIT and fellow graduate students Tao Chen and Xiang Fu at MIT. Margolis wrote the paper with senior author Pulkit Agrawal, who heads the Improbable AI lab at MIT and is the Steven G. In the future, this could enable robots to charge off into the woods on an emergency response mission or climb a flight of stairs to deliver medication to an elderly shut-in. Unlike other methods for controlling a four-legged robot, this two-part system does not require the terrain to be mapped in advance, so the robot can go anywhere. The researchers tested their system on the MIT mini cheetah, a powerful, agile robot built in the lab of Sangbae Kim, professor of mechanical engineering. The novel control system is split into two parts - one that processes real-time input from a video camera mounted on the front of the robot and another that translates that information into instructions for how the robot should move its body. Now, Margolis and his collaborators have developed a system that improves the speed and agility of legged robots as they jump across gaps in the terrain. Although there are some existing methods for incorporating vision into legged locomotion, most of them aren’t really suitable for use with emerging agile robotic systems,” says Gabriel Margolis, a PhD student in the lab of Pulkit Agrawal, professor in the Computer Science and Artificial Intelligence Laboratory (CSAIL) at MIT. For example, stepping in a gap is difficult to avoid if you can’t see it. “In those settings, you need to use vision in order to avoid failure. In recent years, four-legged robots inspired by the movement of cheetahs and other animals have made great leaps forward, yet they still lag behind their mammalian counterparts when it comes to traveling across a landscape with rapid elevation changes. The movement may look effortless, but getting a robot to move this way is an altogether different prospect. A loping cheetah dashes across a rolling field, bounding over sudden gaps in the rugged terrain.
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