# From Crowd Motion Prediction to Robot Navigation in Crowds

arXiv: https://arxiv.org/abs/2303.01424 · HTML: https://mlsanity.com/paper/2303.01424/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Mar 2023.

## Built on by (most cited first, 13 of 13)

- [Parting with Misconceptions about Learning-based Vehicle Motion Planning](https://mlsanity.com/paper/2306.07962/) (2023)
- [InteRACT: Transformer Models for Human Intent Prediction Conditioned on Robot Actions](https://mlsanity.com/paper/2311.12943/) (2023)
- [SICNav-Diffusion: Safe and Interactive Crowd Navigation with Diffusion Trajectory Predictions](https://mlsanity.com/paper/2503.08858/) (2025)
- [Multi-Agent Inverse Reinforcement Learning in Real World Unstructured Pedestrian Crowds](https://mlsanity.com/paper/2405.16439/) (2024)
- [EgoTraj-Bench: Towards Robust Trajectory Prediction Under Ego-view Noisy Observations](https://mlsanity.com/paper/2510.00405/) (2025)
- [ManiCast: Collaborative Manipulation with Cost-Aware Human Forecasting](https://mlsanity.com/paper/2310.13258/) (2023)
- [Recent Advances in Multi-Agent Human Trajectory Prediction: A Comprehensive Review](https://mlsanity.com/paper/2506.14831/) (2025)
- [Visual Forecasting as a Mid-level Representation for Avoidance](https://mlsanity.com/paper/2310.07724/) (2023)
- [Socially-Aware Shared Control Navigation for Assistive Mobile Robots in the Built Environment](https://mlsanity.com/paper/2405.17279/) (2024)
- [RUMOR: Reinforcement learning for Understanding a Model of the Real World for Navigation in Dynamic Environments](https://mlsanity.com/paper/2404.16672/) (2024)
- [HumAIN: Human-Aware Implicit Social Robot Navigation](https://mlsanity.com/paper/2607.07357/) (2026)
- [ACME: A Multi-Cultural, Multi-Embodiment Social-Navigation Dataset](https://mlsanity.com/paper/2607.21964/) (2026)
- [CoCoNav: Conformal Control for Safe Robot Navigation in Crowds](https://mlsanity.com/paper/2608.07751/) (2026)

## Abstract (arXiv)

We focus on robot navigation in crowded environments. To navigate safely and efficiently within crowds, robots need models for crowd motion prediction. Building such models is hard due to the high dimensionality of multiagent domains and the challenge of collecting or simulating interaction-rich crowd-robot demonstrations. While there has been important progress on models for offline pedestrian motion forecasting, transferring their performance on real robots is nontrivial due to close interaction settings and novelty effects on users. In this paper, we investigate the utility of a recent state-of-the-art motion prediction model (S-GAN) for crowd navigation tasks. We incorporate this model into a model predictive controller (MPC) and deploy it on a self-balancing robot which we subject to a diverse range of crowd behaviors in the lab. We demonstrate that while S-GAN motion prediction accuracy transfers to the real world, its value is not reflected on navigation performance, measured with respect to safety and efficiency; in fact, the MPC performs indistinguishably even when using a simple constant-velocity prediction model, suggesting that substantial model improvements might be needed to yield significant gains for crowd navigation tasks. Footage from our experiments can be found at https://youtu.be/mzFiXg8KsZ0.