# Exploring Social Motion Latent Space and Human Awareness for Effective Robot Navigation in Crowded Environments

arXiv: https://arxiv.org/abs/2310.07335 · HTML: https://mlsanity.com/paper/2310.07335/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 29 references that are in the map.
- First posted on arXiv 11 Oct 2023.

## Builds on (most cited first, 12 of 12)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Character Controllers Using Motion VAEs](https://mlsanity.com/paper/2103.14274/) (2021)
- [Crowd-Robot Interaction: Crowd-aware Robot Navigation with Attention-based Deep Reinforcement Learning](https://mlsanity.com/paper/1809.08835/) (2018)
- [Socially Aware Motion Planning with Deep Reinforcement Learning](https://mlsanity.com/paper/1703.08862/) (2017)
- [Self-Supervised Policy Adaptation during Deployment](https://mlsanity.com/paper/2007.04309/) (2020)
- [SocialVAE: Human Trajectory Prediction using Timewise Latents](https://mlsanity.com/paper/2203.08207/) (2022)
- [Intention Aware Robot Crowd Navigation with Attention-Based Interaction Graph](https://mlsanity.com/paper/2203.01821/) (2022)
- [Relational Graph Learning for Crowd Navigation](https://mlsanity.com/paper/1909.13165/) (2019)
- [Decentralized Structural-RNN for Robot Crowd Navigation with Deep Reinforcement Learning](https://mlsanity.com/paper/2011.04820/) (2020)
- [Frozone: Freezing-Free, Pedestrian-Friendly Navigation in Human Crowds](https://mlsanity.com/paper/2003.05395/) (2020)
- [How do walkers avoid a mobile robot crossing their way?](https://mlsanity.com/paper/1609.07955/) (2016)
- [Towards a Unified Planner For Socially-Aware Navigation](https://mlsanity.com/paper/1810.00966/) (2018)

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

- [MessyKitchens: Contact-rich object-level 3D scene reconstruction](https://mlsanity.com/paper/2603.16868/) (2026)
- [Vision-Based Human Awareness Estimation for Enhanced Safety and Efficiency of AMRs in Industrial Warehouses](https://mlsanity.com/paper/2604.18627/) (2026)

## Abstract (arXiv)

This work proposes a novel approach to social robot navigation by learning to generate robot controls from a social motion latent space. By leveraging this social motion latent space, the proposed method achieves significant improvements in social navigation metrics such as success rate, navigation time, and trajectory length while producing smoother (less jerk and angular deviations) and more anticipatory trajectories. The superiority of the proposed method is demonstrated through comparison with baseline models in various scenarios. Additionally, the concept of humans' awareness towards the robot is introduced into the social robot navigation framework, showing that incorporating human awareness leads to shorter and smoother trajectories owing to humans' ability to positively interact with the robot.