Reproduce “GGraph: A GPU-Accelerated Library for Graph-Based Robot Deployment and Learning” on OpenResearch
Turn your favorite coding agents into research agents that review literature, develop hypotheses, and run experiments.
Universal app for Apple silicon and Intel. Requires macOS 11 or later.
About this paper
Yash Rampuria, Rohan Garg, Daniele Gammelli, Marco Pavone
GGraph keeps a changing navigation graph on the GPU for updates and use by planners or learned policies. It merges local observations into a persistent graph and supports loop-closure corrections. At 512 parallel robots, it reaches about 6,500 environment steps per second versus 244 for a CPU pipeline. A PPO policy trained in 2-D grids explored one office floor on Spot over a 157 m path, one reported deployment.
Summary by alphaXiv.
Tip: change arxiv.org to autoarxiv.org in any arXiv abstract link to open the paper here.