MIT and NVIDIA researchers created a GPU-accelerated algorithm that lets robots plan complex tasks in seconds, boosting industrial efficiency.
MIT and NVIDIA Research researchers have developed a powerful new algorithm that drastically accelerates how robots plan their actions.Robots may complete intricate, multistep manipulation tasks in seconds using this technology, which uses the parallel computing capability of graphics processing units to analyze thousands of possible answers simultaneously rather than one at a time.
involves generating a high-level task plan—a sequence of actions—and a corresponding motion plan, which defines specific parameters like joint positions and gripper orientation.A robot must consider many things when packing objects into a box, such as how to grip objects, align them for the best fit, prevent collisions, and adhere to packing order and other constraints. The intricacy of these jobs generates a vast search space.
narrows its focus to more promising solutions that are more likely to satisfy the task’s constraints. This targeted sampling significantly improves the quality of initial candidates.Then, through a parallelized optimization process, cu , by leveraging the power of GPUs, which are far more efficient than CPUs for parallel computation. This allows the system to sample and optimize hundreds or thousands of solutions simultaneously, dramatically improving performance.“With GPUs, optimizing many solutions costs the same as optimizing just one,” Shen told MIT News.In tests involving Tetris-like packing simulations, cu
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