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Faster Training for Robotic Manipulation in GPU Parallelized Robotics Simulation
Abstract
Robotic manipulation is a challenging research area, particularly in developing policies that generalize across diverse real-world scenarios. While real-world data can be slow and unsafe, simulations offer a safer and faster alternative. However, training in simulations can still be time-consuming, hindering rapid model iteration. This paper explores using graphics processing unit (GPU) acceleration to speed up training for robotic manipulation tasks in simulations. By comparing GPU and CPU performance, we demonstrate a significant reduction in training time. The findings show that GPU hardware enhances policy development efficiency, accelerating research and applications, including sim-to-real transfer. Additionally, it broadens exploration of state and action spaces, providing agents with a diverse range of training experiences. A simulation benchmark was also created to test GPU acceleration, detailing task selection, environment setup, and performance measurement. This benchmark forms the basis for evaluating the speedup achieved by GPUs in training robotic manipulation models.
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