The robot faces Towering challenge has long been: the ability to build capacity to adapt to the environment and unpredictable conditions. Despite the progress from the complex program in the 1970s to study deep today, high quality data requirements. Researchers at Mit Science Science Science and Intefent Lab (CSail) of Mit (CSAIL) opened a change to LucidsimAI cutting system and effective movement of the robot training in the virtual environment with a prominent world.
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Appointment ‘SIM-to-Domy’ Diod ‘: How Luciptim Work
One of the durable obstacles in the robot competition is ‘Sim-to-Deli and Deli’– Difficulty transferring skills learned in simulation with a complicated and dynamic world. Traditional simulation often lacks the truth that needs to prepare robots for real global conditions. Lucidsim mentioned this issue by combining Simulators Lead AI version of AI versionCreate a very diverse and realistic training environment.
Using Lucidsim Large model To create a detailed description of the environment of the environment, which then converts into the image. These photos are updated with a deep map and semantic mask, providing geometry information and conditions. To fill the robot in real situations, systems that help with novel techniques called Dreams in movement (dim)Which creates a short video, consistent video simulates the robot’s view of movement.
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Lucidsim’s birth: from the idea to innovation
Inspiration for Lucidsim caused by a conventional conversation outside the Taquera bag. Alan Yu, Bachelor Degree, and Geo Yang, Support Members, Discuss the way to train the robot. We feel that “We want to teach a robot with the vision of the human vision. But we know that we do not have a development of the development of Lucidsim, security systems Diversity of data and reality of sight.
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Lucidsim’s Better Performance in the real test of the world
Research teams have tested Lucidsim by trained in challenging tasks, such as staircase, identify obstacles, and overcome obstacles. The result shows its superiority:
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Obstacle Navigation: The robot has been trained with Lucidsim attach success rate 88%, compared to only 15% using the teaching methods that teach experts.
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Stair Stairs: Lucidsim-Enrained robot successfully successfully, which is a traditional system of fighting.
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Profile Performance: Twice of the training information in Lucidsim shows a widespread improvement, showing its ability.
Collection of self-compiling information that can only be used without just learning higher but also trained higher-effective training.
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Brief History of robot development
The evolution of the robot is marked by major mailstones:
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1921: The word “robot” was introduced by the Czech Karel Ackwright Karel aspek in his play “Rir ‘, demonstrates artificial workers.
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1954: George Devol has officimed the first operational robot and the program, later known as absence of abilities.
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1961: Unity is used in motorcycle factories, which is the first of the industrial robot.
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1973: Kuka robot develops hunger, one of the most common robots with driving 6 cuttings.
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The 1980s: The combination of robots into the production process becomes widespread spread. The number of industrial robots in the United States increased from about 200 years in 1970 to nearly 4,000 to 1980.
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The 2000s: 2000: Advanced learning intelligence and artificial machines have made development of independent and intelligent robots.
These points are deeply highlighted of innovation in robotsics, leading to systems such as the scope of learning and adjustment of the machine.
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Applications beyond the use with ends
While Lucidsim has been tested in the quadrupped robot that performs the park-like work, potential potential. Researchers intended to use the system Mobile manipulationMake the robot can make the robot can handle objects in active environments, such as warehouse or home. This application requires a master Perception of color And the interaction of intenditic objects – the domain also relied on the world protest realistic.
The money of Lucidsim can reform this field by making real collection for various tasks, overcome physical training situations.
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Important hospitality and future directions
Lucidsim has collected from experts in Robotoms and Ai. SHURAN song, Assistant Professor at Stanford University, praised the system capacity Multiple and realistic informationSpeed the use of trained robots in the virtual environment. “One of the main challenges in the robot transfer for the robotics is relieved by any of the simulation. Song
The next step of the team consists of a robotic trainer and search the program in the robot arm requires an engine skills, such as concentrated objects. These progress can determine the role of robots in the industry since production to service.
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Funding and Research Support
Lucidsim’s development has been supported by the Wide range of institutions, including National Science Foundation, Naval Research OfficeAnd Amazon. Research was conducted by the CSAIL team, in which there was Yang, Alan Yu, Yajvan Ravan, John Leonard, and Phillip Isola. Results have been introduced in the famous Robot Learning Meeting (Corl) At the beginning of this month.
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Source: Technology Review, To the csail, Wikipedia, National Hall of Fame Office, The idea., Cry, Chief