14 papers with code • 0 benchmarks • 1 datasets
This task studies how to navigate robot(s) among humans in a safe and socially acceptable way.
These leaderboards are used to track progress in Social Navigation
LibrariesUse these libraries to find Social Navigation models and implementations
Navigating fluently around pedestrians is a necessary capability for mobile robots deployed in human environments, such as buildings and homes.
Mimicking human ability to forecast future positions or interpret complex interactions in urban scenarios, such as streets, shopping malls or squares, is essential to develop socially compliant robots or self-driving cars.
In contrast to self-empowerment, a robot employing our approach strives for the empowerment of people in its environment, so they are not disturbed by the robot's presence and motion.
SoLo T-DIRL: Socially-Aware Dynamic Local Planner based on Trajectory-Ranked Deep Inverse Reinforcement Learning
This work proposes a new framework for a socially-aware dynamic local planner in crowded environments by building on the recently proposed Trajectory-ranked Maximum Entropy Deep Inverse Reinforcement Learning (T-MEDIRL).
In this work, we propose Social Robot Tree Search (SoRTS), an algorithm for the safe navigation of mobile robots in social domains.