Welcome to the Social Navigation Benchmark utility (SocNavBench), a codebase for benchmarking robot planning algorithms against various episodes of containing multi-agent environments, accompanying out paper "SocNavBench: A Grounded Simulation Testing Framework for Evaluating Social Navigation".
We provide scenarios curated from real world data for social navigation algorithms to be tested and evaluated on.
We also provide multiple curated maps that closely resemble the environments for the pedestrian datasets.
Guide for installation at INSTALLATION.md
Guide for usage at USAGE.md
This work was funded under grants from the National Science Foundation (NSF IIS-1734361) and the National Institute on Disability, Independent Living, and Rehabilitation Research (NIDILRR 90DPGE0003).
This project is also built upon the Human Active Navigation (HumANav) and Learning-Based Waypoint Navigation (Visual-Navigation) codebases. Special thanks to Varun Tolani for helping us with his projects.