Wednesday, October 20, 2021

g-f(2)583 THE BIG PICTURE OF THE DIGITAL AGE (10/20/2021), MIT News, One giant leap for the mini cheetah

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"g-f" fishing of golden knowledge (GK) of the fabulous treasure of the digital ageArtificial Intelligence, Robotic automation (10/20/2021)  g-f(2)426 


Robotic automation, MIT News 

  • Gabriel Margolis (a PhD student in the lab of Pulkit Agrawal, professor in the Computer Science and Artificial Intelligence Laboratory (CSAIL) at MIT) and his collaborators have developed a system that improves the speed and agility of legged robots as they jump across gaps in the terrain. 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. 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.
  • 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. 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.

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                      One giant leap for the mini cheetah, Massachusetts Institute of Technology (MIT)


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                      Lessons learned, MIT News 

                      • The use of two separate controllers working together makes this system especially innovative.
                      • A controller is an algorithm that will convert the robot’s state into a set of actions for it to follow. Many blind controllers — those that do not incorporate vision — are robust and effective but only enable robots to walk over continuous terrain.
                      • Vision is such a complex sensory input to process that these algorithms are unable to handle it efficiently. 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.
                      • Margolis wrote the paper with senior author Pulkit Agrawal, who heads the Improbable AI lab at MIT and is the Steven G. 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. 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. The work will be presented next month at the Conference on Robot Learning.

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                      Teaching the network, MIT News 

                      • The researchers used the trial-and-error method known as reinforcement learning to train the high-level controller. They conducted simulations of the robot running across hundreds of different discontinuous terrains and rewarded it for successful crossings.
                      • Over time, the algorithm learned which actions maximized the reward.
                      • Then they built a physical, gapped terrain with a set of wooden planks and put their control scheme to the test using the mini cheetah.
                      • Their system outperformed others that only use one controller, and the mini cheetah successfully crossed 90 percent of the terrains.

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                      • [genioux fact deduced or extracted from MIT News]
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                      “genioux facts”: The online programme on MASTERING “THE BIG PICTURE OF THE DIGITAL AGE”, g-f(2)583, Fernando Machuca, October 20, 2021, Corporation.

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