# Reproducibility Report: Contrastive Learning of Socially-aware Motion Representations

arXiv: https://arxiv.org/abs/2208.09284 · HTML: https://mlsanity.com/paper/2208.09284/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 10 references that are in the map.
- First posted on arXiv 18 Aug 2022.

## Builds on (most cited first, 6 of 6)

- [Rainbow: Combining Improvements in Deep Reinforcement Learning](https://mlsanity.com/paper/1710.02298/) (2017)
- [Social-STGCNN: A Social Spatio-Temporal Graph Convolutional Neural Network for Human Trajectory Prediction](https://mlsanity.com/paper/2002.11927/) (2020)
- [Crowd-Robot Interaction: Crowd-aware Robot Navigation with Attention-based Deep Reinforcement Learning](https://mlsanity.com/paper/1809.08835/) (2018)
- [Multimodal Trajectory Prediction Conditioned on Lane-Graph Traversals](https://mlsanity.com/paper/2106.15004/) (2021)
- [Trajectron++: Dynamically-Feasible Trajectory Forecasting With Heterogeneous Data](https://mlsanity.com/paper/2001.03093/) (2020)
- [Social NCE: Contrastive Learning of Socially-aware Motion Representations](https://mlsanity.com/paper/2012.11717/) (2020)

## Abstract (arXiv)

The following paper is a reproducibility report for "Social NCE: Contrastive Learning of Socially-aware Motion Representations" citeliu2020snce published in ICCV 2021 as part of the ML Reproducibility Challenge 2021. The original code was made available by the author footnotehrefhttps://github.com/vita-epfl/social-ncehttps://github.com/vita-epfl/social-nce. We attempted to verify the results claimed by the authors and reimplemented their code in PyTorch Lightning.