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/
@article{sanchez2026opensocint,title={OpenSocInt: A Multi-modal Training Environment for Human-Aware Social Navigation},author={Sanchez, Victor and Reinke, Chris and Mohamed, Ahamed and Alameda-Pineda, Xavier},journal={arXiv preprint arXiv:2601.01939},url={https://arxiv.org/pdf/2601.01939},year={2026},}
2023
Deep Reinforcement Learning on Social Environment Aware Navigation based on Maps
@misc{sanchez2023deep,title={Deep Reinforcement Learning on Social Environment Aware Navigation based on Maps},author={Sanchez, Victor},year={2023},url={https://www.diva-portal.org/smash/record.jsf?pid=diva2%3A1763094&dswid=1306},}