首页|Data from University of Texas Austin Provide New Insights into Robotics (On Seco nd-order Derivatives of Rigid-body Dynamics: Theory and Implementation)
Data from University of Texas Austin Provide New Insights into Robotics (On Seco nd-order Derivatives of Rigid-body Dynamics: Theory and Implementation)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Robotics is the subjec t of a report. According to news reporting out of Austin, Texas, by NewsRx edito rs, research stated, “Model-based control for robots has increasingly depended o n optimization-based methods, such as differential dynamic programming (DDP) and iterative LQR (iLQR). These methods can form the basis of model-predictive cont rol, which is commonly used for controlling legged robots.” Financial support for this research came from National Science Foundation (NSF).
AustinTexasUnited StatesNorth and Central AmericaEmerging TechnologiesMachine LearningNano-robotRobotRob oticsUniversity of Texas Austin
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