# SG-LSTM: Social Group LSTM for Robot Navigation Through Dense Crowds

arXiv: https://arxiv.org/abs/2303.04320 · HTML: https://mlsanity.com/paper/2303.04320/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 56 references that are in the map.
- First posted on arXiv 8 Mar 2023.

## Builds on (most cited first, 16 of 16)

- [Socially Aware Motion Planning with Deep Reinforcement Learning](https://mlsanity.com/paper/1703.08862/) (2017)
- [MOTChallenge 2015: Towards a Benchmark for Multi-Target Tracking](https://mlsanity.com/paper/1504.01942/) (2015)
- [Decentralized Non-communicating Multiagent Collision Avoidance with Deep Reinforcement Learning](https://mlsanity.com/paper/1609.07845/) (2016)
- [TraPHic: Trajectory Prediction in Dense and Heterogeneous Traffic Using Weighted Interactions](https://mlsanity.com/paper/1812.04767/) (2018)
- [Socially Compliant Navigation through Raw Depth Inputs with Generative Adversarial Imitation Learning](https://mlsanity.com/paper/1710.02543/) (2017)
- [PORCA: Modeling and Planning for Autonomous Driving among Many Pedestrians](https://mlsanity.com/paper/1805.11833/) (2018)
- [CMetric: A Driving Behavior Measure Using Centrality Functions](https://mlsanity.com/paper/2003.04424/) (2020)
- [Frozone: Freezing-Free, Pedestrian-Friendly Navigation in Human Crowds](https://mlsanity.com/paper/2003.05395/) (2020)
- [GraphRQI: Classifying Driver Behaviors Using Graph Spectrums](https://mlsanity.com/paper/1910.00049/) (2019)
- [SocioSense: Robot Navigation Amongst Pedestrians with Social and Psychological Constraints](https://mlsanity.com/paper/1706.01102/) (2017)
- [CoMet: Modeling Group Cohesion for Socially Compliant Robot Navigation in Crowded Scenes](https://mlsanity.com/paper/2108.09848/) (2021)
- [Identifying Driver Behaviors using Trajectory Features for Vehicle Navigation](https://mlsanity.com/paper/1803.00881/) (2018)
- [Pedestrian Dominance Modeling for Socially-Aware Robot Navigation](https://mlsanity.com/paper/1810.06613/) (2018)
- [Realtime Multilevel Crowd Tracking using Reciprocal Velocity Obstacles](https://mlsanity.com/paper/1402.2826/) (2014)
- [Early Recognition of Human Activities from First-Person Videos Using Onset Representations](https://mlsanity.com/paper/1406.5309/) (2014)
- [The Socially Invisible Robot: Navigation in the Social World using Robot Entitativity](https://mlsanity.com/paper/1805.05543/) (2018)

## Built on by (most cited first, 2 of 2)

- [SACSoN: Scalable Autonomous Control for Social Navigation](https://mlsanity.com/paper/2306.01874/) (2023)
- [Principles and Guidelines for Evaluating Social Robot Navigation Algorithms](https://mlsanity.com/paper/2306.16740/) (2023)

## Abstract (arXiv)

With the increasing availability and affordability of personal robots, they will no longer be confined to large corporate warehouses or factories but will instead be expected to operate in less controlled environments alongside larger groups of people. In addition to ensuring safety and efficiency, it is crucial to minimize any negative psychological impact robots may have on humans and follow unwritten social norms in these situations. Our research aims to develop a model that can predict the movements of pedestrians and perceptually-social groups in crowded environments. We introduce a new Social Group Long Short-term Memory (SG-LSTM) model that models human groups and interactions in dense environments using a socially-aware LSTM to produce more accurate trajectory predictions. Our approach enables navigation algorithms to calculate collision-free paths faster and more accurately in crowded environments. Additionally, we also release a large video dataset with labeled pedestrian groups for the broader social navigation community. We show comparisons with different metrics on different datasets (ETH, Hotel, MOT15) and different prediction approaches (LIN, LSTM, O-LSTM, S-LSTM) as well as runtime performance.