SMARTlab-Purdue/SAN-FAPL
This repository contains the source code for our paper: "Feedback-efficient Active Preference Learning for Socially Aware Robot Navigation", accepted to IROS-2022. For more details, please refer to our project website at https://sites.google.com/view/san-fapl.
This project helps roboticists develop navigation systems that allow robots to move comfortably and safely around people. By taking human feedback on robot behavior, it learns what constitutes 'socially acceptable' movement. The output is a refined reward model that guides the robot's navigation, improving human-robot interaction in shared spaces. This is for researchers and engineers working on autonomous robots in human-centric environments.
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Use this if you need to train robots to navigate intuitively and respectfully alongside humans, without relying on complex, pre-programmed social rules or extensive demonstrations.
Not ideal if your robot operates in environments without human interaction or if its primary objective is speed and obstacle avoidance without social considerations.
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Oct 17, 2022
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