# A Comparative Study of Human Motion Models in Reinforcement Learning Algorithms for Social Robot Navigation

arXiv: https://arxiv.org/abs/2503.15127 · HTML: https://mlsanity.com/paper/2503.15127/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods.
- Builds on 19 of its 46 references that are in the map.
- First posted on arXiv 19 Mar 2025.

## Placed in

- Deep reinforcement learning methods / [RL for robot navigation and UAV control](https://mlsanity.com/deeprl/rl-for-robot-navigation-and-uav-control/)

## Builds on (most cited first, 19 of 19)

- [On Evaluation of Embodied Navigation Agents](https://mlsanity.com/paper/1807.06757/) (2018)
- [Human Motion Trajectory Prediction: A Survey](https://mlsanity.com/paper/1905.06113/) (2019)
- [Social Attention: Modeling Attention in Human Crowds](https://mlsanity.com/paper/1710.04689/) (2017)
- [Crowd-Robot Interaction: Crowd-aware Robot Navigation with Attention-based Deep Reinforcement Learning](https://mlsanity.com/paper/1809.08835/) (2018)
- [Decentralized Non-communicating Multiagent Collision Avoidance with Deep Reinforcement Learning](https://mlsanity.com/paper/1609.07845/) (2016)
- [Motion Planning Among Dynamic, Decision-Making Agents with Deep Reinforcement Learning](https://mlsanity.com/paper/1805.01956/) (2018)
- [Core Challenges of Social Robot Navigation: A Survey](https://mlsanity.com/paper/2103.05668/) (2021)
- [Principles and Guidelines for Evaluating Social Robot Navigation Algorithms](https://mlsanity.com/paper/2306.16740/) (2023)
- [Socially Compliant Navigation through Raw Depth Inputs with Generative Adversarial Imitation Learning](https://mlsanity.com/paper/1710.02543/) (2017)
- [A Survey on Socially Aware Robot Navigation: Taxonomy and Future Challenges](https://mlsanity.com/paper/2311.06922/) (2023)
- [A Survey on Human-aware Robot Navigation](https://mlsanity.com/paper/2106.11650/) (2021)
- [Model Predictive Contouring Control for Collision Avoidance in Unstructured Dynamic Environments](https://mlsanity.com/paper/2010.10190/) (2020)
- [HuNavSim: A ROS 2 Human Navigation Simulator for Benchmarking Human-Aware Robot Navigation](https://mlsanity.com/paper/2305.01303/) (2023)
- [Leveraging Neural Network Gradients within Trajectory Optimization for Proactive Human-Robot Interactions](https://mlsanity.com/paper/2012.01027/) (2020)
- [Generating Reliable and Efficient Predictions of Human Motion: A Promising Encounter between Physics and Neural Networks](https://mlsanity.com/paper/2006.08429/) (2020)
- [MengeROS: a Crowd Simulation Tool for Autonomous Robot Navigation](https://mlsanity.com/paper/1801.08823/) (2018)
- [Crowd against the machine: A simulation-based benchmark tool to evaluate and compare robot capabilities to navigate a human crowd](https://mlsanity.com/paper/2104.14177/) (2021)
- [Proactive Opinion-Driven Robot Navigation around Human Movers](https://mlsanity.com/paper/2210.01642/) (2022)
- [Simulators for Mobile Social Robots:State-of-the-Art and Challenges](https://mlsanity.com/paper/2202.03582/) (2022)

## Abstract (arXiv)

Social robot navigation is an evolving research field that aims to find efficient strategies to safely navigate dynamic environments populated by humans. A critical challenge in this domain is the accurate modeling of human motion, which directly impacts the design and evaluation of navigation algorithms. This paper presents a comparative study of two popular categories of human motion models used in social robot navigation, namely velocity-based models and force-based models. A system-theoretic representation of both model types is presented, which highlights their common feedback structure, although with different state variables. Several navigation policies based on reinforcement learning are trained and tested in various simulated environments involving pedestrian crowds modeled with these approaches. A comparative study is conducted to assess performance across multiple factors, including human motion model, navigation policy, scenario complexity and crowd density. The results highlight advantages and challenges of different approaches to modeling human behavior, as well as their role during training and testing of learning-based navigation policies. The findings offer valuable insights and guidelines for selecting appropriate human motion models when designing socially-aware robot navigation systems.