Social Navigation
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.
Benchmarks
These leaderboards are used to track progress in Social Navigation
Libraries
Use these libraries to find Social Navigation models and implementations
HabiCrowd
Most implemented papers
Habitat 3.0: A Co-Habitat for Humans, Avatars and Robots
facebookresearch/habitat-sim
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We present Habitat 3. 0: a simulation platform for studying collaborative human-robot tasks in home environments.
Robot Navigation in Constrained Pedestrian Environments using Reinforcement Learning
StanfordVL/iGibsonChallenge2021
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Navigating fluently around pedestrians is a necessary capability for mobile robots deployed in human environments, such as buildings and homes.
Decentralized Structural-RNN for Robot Crowd Navigation with Deep Reinforcement Learning
Shuijing725/CrowdNav_DSRNN
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Safe and efficient navigation through human crowds is an essential capability for mobile robots.
Intention Aware Robot Crowd Navigation with Attention-Based Interaction Graph
Shuijing725/CrowdNav_Prediction_AttnGraph
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We study the problem of safe and intention-aware robot navigation in dense and interactive crowds.
Social and Scene-Aware Trajectory Prediction in Crowded Spaces
Oghma/sns-lstm
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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.
Social Navigation with Human Empowerment driven Deep Reinforcement Learning
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.
Visual Navigation Among Humans with Optimal Control as a Supervisor
Videos describing our approach and experiments, as well as a demo of HumANav are available on the project website.
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).
SOCIALGYM 2.0: Simulator for Multi-Agent Social Robot Navigation in Shared Human Spaces
We present SocialGym 2, a multi-agent navigation simulator for social robot research.
SocNavGym: A Reinforcement Learning Gym for Social Navigation
We propose SocNavGym, an advanced simulation environment for social navigation that can generate a wide variety of social navigation scenarios and facilitates the development of intelligent social agents.
