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GGraph Open-Source GPU-Accelerated Graph Library Supports Robot Deployment and Training
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The author team of Yash Rampuria, Rohan Garg, Daniele Gammelli, Marco Pavone, and others released the open-source GPU-accelerated graph library GGraph (arXiv:2610.28953), aimed at robot deployment and large-scale learning. GGraph supports real robots through a ROS 2 interface and supports vectorized GPU policy training, with built-in support for tasks such as 3D navigation, exploration, and loop closure integration. The author team states that for a single robot, graph update runtime is on the order of milliseconds; in large-scale training, it supports hundreds of parallel robots training graph-based exploration policies, reducing training wall-clock time by an order of magnitude. The team states that these results were validated in simulation and real-robot experiments.
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- GGraph open-source GPU-accelerated graph library supports robot deployment and large-scale learning
The authors' team has released GGraph, an open-source GPU-accelerated graph library for robot deployment and large-scale learning. It supports real robots through ROS 2 interfaces and supports vectorized GPU policy training, with built-in support for tasks including 3D navigation, exploration, and loop-closure integration. In single-robot operation, graph updates take milliseconds; in large-scale training, it supports hundreds of parallel robots training graph-based exploration policies, reducing wall-clock training time by an order of magnitude.
arXiv cs.RO ·