SocialNav-Map: Dynamic Mapping with Human Trajectory Prediction for Zero-Shot Social Navigation

Repository for SocialNav-Map: Dynamic Mapping with Human Trajectory Prediction for Zero-Shot Social Navigation

Social navigation in densely populated dynamic environments poses a significant challenge for autonomous mo- bile robots, requiring advanced strategies for safe interaction. Existing reinforcement learning (RL)-based methods require over 2000+ hours of extensive training and often struggle to generalize to unfamiliar environments without additional fine- tuning, limiting their practical application in real-world scenar- ios. To address these limitations, we propose SocialNav-Map, a novel zero-shot social navigation framework that combines dynamic human trajectory prediction with occupancy mapping, enabling safe and efficient navigation without the need for environment-specific training. Specifically, SocialNav-Map first transforms the task goal position into the constructed map co- ordinate system. Subsequently, it creates a dynamic occupancy map that incorporates predicted human movements as dynamic obstacles. The framework employs two complementary methods for human trajectory prediction: history prediction and orien- tation prediction. By integrating these predicted trajectories into the occupancy map, the robot can proactively avoid potential collisions with humans while efficiently navigating to its destination. Extensive experiments on the Social-HM3D and Social-MP3D datasets demonstrate that SocialNav-Map signifi- cantly outperforms state-of-the-art (SOTA) RL-based methods, which require 2,396 GPU hours of training. Notably, it reduces human collision rates by over 10% without necessitating any training in novel environments. By eliminating the need for environment-specific training, SocialNav-Map achieves superior navigation performance, paving the way for the deployment of social navigation systems in real-world environments charac- terized by diverse human behaviors.

Getting Started

1. Preparing conda env

Assuming you have conda installed, let's prepare a conda env:

conda_env_name=socialnav-map
conda create -n $conda_env_name python=3.9 cmake=3.14.0
conda activate $conda_env_name

2. conda install habitat-sim & habitat-lab

Following Habitat-lab's instruction:

conda install habitat-sim=0.3.1 withbullet headless -c conda-forge -c aihabitat

If you encounter network problems, you can manually download the Conda package from this link to download the conda bag, and install it via: conda install --use-local /path/to/xxx.tar.bz2 to download.

Then, assuming you have this repositories cloned (forked from Habitat 3.0), install necessary dependencies of Habitat.

cd Falcon
pip install -e habitat-lab
pip install -e habitat-baselines
pip install -r requirements.txt # install other dependencies

For other dependencies, see socialnav-map.yaml.

3. Downloading the Social-HM3D & Social-MP3D datasets

• Download Scene Datasets

Following the instructions for HM3D and MatterPort3D in Habitat-lab's Datasets.md.

• Download Episode Datasets

Download social navigation (SocialNav) episodes for the test scenes, which can be found here: Link.

After downloading, unzip and place the datasets in the default location:

unzip -d data/datasets/pointnav

• Download Leg animation

wget https://github.com/facebookresearch/habitat-lab/files/12502177/spot_walking_trajectory.csv -O data/robots/spot_data/spot_walking_trajectory.csv

• Download Multi-agent necessary data

python -m habitat_sim.utils.datasets_download --uids hab3-episodes habitat_humanoids hab3_bench_assets hab_spot_arm

The file structure should look like this:

SocialNav-Map/
└── Falcon/
└── socialnav-map/
    └── data/
        ├── datasets
        │   └── pointnav
        │       ├── social-hm3d
        │       │   ├── train
        │       │   │   ├── content
        │       │   │   └── train.json.gz
        │       │   └── val
        │       │       ├── content
        │       │       └── val.json.gz
        │       └── social-mp3d
        │           ├── train
        │           │   ├── content
        │           │   └── train.json.gz
        │           └── val
        │               ├── content
        │               └── val.json.gz
        ├── scene_datasets
        ├── robots
        ├── humanoids
        ├── versoned_data
        └── hab3_bench_assets

For Evaluation

cd socialnav-map
python socialmap_hm3d.py --split val --eval 1 --auto_gpu_config 0 -n 1 --num_eval_episodes 400  --num_local_steps 10 --print_images 1  --exp_name ronghe_0.1 --eval_episodes 1087 --collect 0 --stop_th 494

Acknowledgement

Our code is modified from InstructNav and Falcon. Thanks for their open-set work.

BibTex

Please cite our paper if you find it helpful :)

@article{zhang2025socialnav,
  title={SocialNav-Map: Dynamic Mapping with Human Trajectory Prediction for Zero-Shot Social Navigation},
  author={Zhang, Lingfeng and Xiao, Erjia and Hao, Xiaoshuai and Fu, Haoxiang and Gong, Zeying and Chen, Long and Liang, Xiaojun and Xu, Renjing and Ye, Hangjun and Ding, Wenbo},
  journal={arXiv preprint arXiv:2511.12232},
  year={2025}
}