# A Deep Learning Approach To Multi-Context Socially-Aware Navigation

arXiv: https://arxiv.org/abs/2104.10197 · HTML: https://mlsanity.com/paper/2104.10197/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Apr 2021.

## Built on by (most cited first, 3 of 3)

- [Learning Social Cost Functions for Human-Aware Path Planning](https://mlsanity.com/paper/2407.10547/) (2024)
- [Adaptive Social Force Window Planner with Reinforcement Learning](https://mlsanity.com/paper/2404.13678/) (2024)
- [The Social Context of Human-Robot Interactions](https://mlsanity.com/paper/2508.13982/) (2025)

## Abstract (arXiv)

We present a context classification pipeline to allow a robot to change its navigation strategy based on the observed social scenario. Socially-Aware Navigation considers social behavior in order to improve navigation around people. Most of the existing research uses different techniques to incorporate social norms into robot path planning for a single context. Methods that work for hallway behavior might not work for approaching people, and so on. We developed a high-level decision-making subsystem, a model-based context classifier, and a multi-objective optimization-based local planner to achieve socially-aware trajectories for autonomously sensed contexts. Using a context classification system, the robot can select social objectives that are later used by Pareto Concavity Elimination Transformation (PaCcET) based local planner to generate safe, comfortable, and socially-appropriate trajectories for its environment. This was tested and validated in multiple environments on a Pioneer mobile robot platform; results show that the robot was able to select and account for social objectives related to navigation autonomously.