# OpenSocInt: A Multi-modal Training Environment for Human-Aware Social Navigation

arXiv: https://arxiv.org/abs/2601.01939 · HTML: https://mlsanity.com/paper/2601.01939/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 6 references that are in the map.
- First posted on arXiv 5 Jan 2026.

## Builds on (most cited first, 3 of 3)

- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [On the Sample Complexity of Actor-Critic Method for Reinforcement Learning with Function Approximation](https://mlsanity.com/paper/1910.08412/) (2019)

## Abstract (arXiv)

In this paper, we introduce OpenSocInt, an open-source software package providing a simulator for multi-modal social interactions and a modular architecture to train social agents. We described the software package and showcased its interest via an experimental protocol based on the task of social navigation. Our framework allows for exploring the use of different perceptual features, their encoding and fusion, as well as the use of different agents. The software is already publicly available under GPL at https://gitlab.inria.fr/robotlearn/OpenSocInt/.