# Conflict Avoidance in Social Navigation -- a Survey

arXiv: https://arxiv.org/abs/2106.12113 · HTML: https://mlsanity.com/paper/2106.12113/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 189 references that are in the map.
- First posted on arXiv 23 Jun 2021.

## Builds on (most cited first, 24 of 24)

- [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)
- [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)
- [Towards Optimally Decentralized Multi-Robot Collision Avoidance via Deep Reinforcement Learning](https://mlsanity.com/paper/1709.10082/) (2017)
- [Motion Planning and Control for Mobile Robot Navigation Using Machine Learning: a Survey](https://mlsanity.com/paper/2011.13112/) (2020)
- [Semantics for Robotic Mapping, Perception and Interaction: A Survey](https://mlsanity.com/paper/2101.00443/) (2021)
- [Socially Compliant Navigation through Raw Depth Inputs with Generative Adversarial Imitation Learning](https://mlsanity.com/paper/1710.02543/) (2017)
- [The EMPATHIC Framework for Task Learning from Implicit Human Feedback](https://mlsanity.com/paper/2009.13649/) (2020)
- [Scaling Local Control to Large-Scale Topological Navigation](https://mlsanity.com/paper/1909.12329/) (2019)
- [A Survey on Human-aware Robot Navigation](https://mlsanity.com/paper/2106.11650/) (2021)
- [A Review and Analysis of Eye-Gaze Estimation Systems, Algorithms and Performance Evaluation Methods in Consumer Platforms](https://mlsanity.com/paper/1708.01817/) (2017)
- [SEAN: Social Environment for Autonomous Navigation](https://mlsanity.com/paper/2009.04300/) (2020)
- [Understanding Teacher Gaze Patterns for Robot Learning](https://mlsanity.com/paper/1907.07202/) (2019)
- [Mapless Navigation among Dynamics with Social-safety-awareness: a reinforcement learning approach from 2D laser scans](https://mlsanity.com/paper/1911.03074/) (2019)
- [Hierarchical Reinforcement Learning Framework towards Multi-agent Navigation](https://mlsanity.com/paper/1807.05424/) (2018)
- [SocioSense: Robot Navigation Amongst Pedestrians with Social and Psychological Constraints](https://mlsanity.com/paper/1706.01102/) (2017)
- [Long-Term Robot Navigation in Indoor Environments Estimating Patterns in Traversability Changes](https://mlsanity.com/paper/1909.12733/) (2019)
- [Following Social Groups: Socially Compliant Autonomous Navigation in Dense Crowds](https://mlsanity.com/paper/1911.12063/) (2019)
- [Mobile Robot Path Planning in Dynamic Environments: A Survey](https://mlsanity.com/paper/2006.14195/) (2020)
- [Role Playing Learning for Socially Concomitant Mobile Robot Navigation](https://mlsanity.com/paper/1705.10092/) (2017)
- [Autonomous Social Distancing in Urban Environments using a Quadruped Robot](https://mlsanity.com/paper/2008.08889/) (2020)
- [Unclogging Our Arteries: Using Human-Inspired Signals to Disambiguate Navigational Intentions](https://mlsanity.com/paper/1909.06560/) (2019)

## Built on by (most cited first, 14 of 14)

- [Principles and Guidelines for Evaluating Social Robot Navigation Algorithms](https://mlsanity.com/paper/2306.16740/) (2023)
- [VLM-Social-Nav: Socially Aware Robot Navigation through Scoring using Vision-Language Models](https://mlsanity.com/paper/2404.00210/) (2024)
- [SOCIALMAPF: Optimal and Efficient Multi-Agent Path Finding with Strategic Agents for Social Navigation](https://mlsanity.com/paper/2210.08390/) (2022)
- [VANP: Learning Where to See for Navigation with Self-Supervised Vision-Action Pre-Training](https://mlsanity.com/paper/2403.08109/) (2024)
- [SocialNav-MoE: A Mixture-of-Experts Vision Language Model for Socially Compliant Navigation with Reinforcement Fine-Tuning](https://mlsanity.com/paper/2512.14757/) (2025)
- [A Study on Learning Social Robot Navigation with Multimodal Perception](https://mlsanity.com/paper/2309.12568/) (2023)
- [Arena 4.0: A Comprehensive ROS2 Development and Benchmarking Platform for Human-centric Navigation Using Generative-Model-based Environment Generation](https://mlsanity.com/paper/2409.12471/) (2024)
- [Artificial Intelligent Disobedience: Rethinking the Agency of Our Artificial Teammates](https://mlsanity.com/paper/2506.22276/) (2025)
- [Dyna-LfLH: Learning Agile Navigation in Dynamic Environments from Learned Hallucination](https://mlsanity.com/paper/2403.17231/) (2024)
- [Learning Contact-based Navigation in Crowds](https://mlsanity.com/paper/2303.01455/) (2023)
- [NavG: Risk-Aware Navigation in Crowded Environments Based on Reinforcement Learning with Guidance Points](https://mlsanity.com/paper/2503.02111/) (2025)
- [HumAIN: Human-Aware Implicit Social Robot Navigation](https://mlsanity.com/paper/2607.07357/) (2026)
- [Failing Gracefully: Mitigating Impact of Inevitable Robot Failures](https://mlsanity.com/paper/2608.05313/) (2026)
- [CoCoNav: Conformal Control for Safe Robot Navigation in Crowds](https://mlsanity.com/paper/2608.07751/) (2026)

## Abstract (arXiv)

A major goal in robotics is to enable intelligent mobile robots to operate smoothly in shared human-robot environments. One of the most fundamental capabilities in service of this goal is competent navigation in this ``social" context. As a result, there has been a recent surge of research on social navigation; and especially as it relates to the handling of conflicts between agents during social navigation. These developments introduce a variety of models and algorithms, however as this research area is inherently interdisciplinary, many of the relevant papers are not comparable and there is no shared standard vocabulary. This survey aims to bridge this gap by introducing such a common language, using it to survey existing work, and highlighting open problems. It starts by defining the boundaries of this survey to a limited, yet highly common type of social navigation - conflict avoidance. Within this proposed scope, this survey introduces a detailed taxonomy of the conflict avoidance components. This survey then maps existing work into this taxonomy, while discussing papers using its framing. Finally, this paper proposes some future research directions and open problems that are currently on the frontier of social navigation to aid ongoing and future research.