Official code release for the paper Dynamic Objects Relocalization in Changing Environments with Flow Matching (code‑name: FlowMaps).

• Installation
• Dataset Generation (FlowSim)
• Configuration
• Training & Logging
• Evaluation & Reproducing Results
• Citation
• License

# 1) Clone
git clone https://github.com/Fra-Tsuna/flowmaps
cd flowmaps
# 2) Create an environment (example with conda) and install deps
conda create -n flowmaps python=3.13 -y
conda activate flowmaps
pip install -r requirements.txt

To generate datasets with FlowSim:

python3 data.py

This creates training/validation data according to config/config.yaml. We use a Hydra‑style syntax for overrides (key=value). You can edit config/config.yaml directly or pass overrides at the CLI.

python3 data.py n_env_train=200 n_env_val=20 max_timesteps=20 stochastic=true

Key parameters:

• n_env_train, n_env_val: number of training and validation environments (respectively).
• max_timesteps: time horizon of object trajectories.
• stochastic: enable stochastic object behavior.
• size: environment canvas size.
• max_tables, min_each: number of furniture instances and minimum per type.
• display_scale: visualization only; upscales the rendered image. Underlying data remain at size×size.

Our project supports Weights & Biases logging.

• Online (default below):

  python3 main.py wandb.mode=online wandb.project=YOUR_PROJECT wandb.entity=YOUR_ENTITY wandb.tags="[flowmaps,cdit]" experiment=cdit

• Offline:

  python3 main.py wandb.mode=offline experiment=cdit

To train the MLP baseline, simply override experiment=mlp

After training, place your checkpoint file in ckpt/ and run:

python3 eval.py checkpoint_name=YOUR_CHECKPOINT_FILENAME

If you find this work useful, please cite the paper:

@misc{argenziano2025dynamicobjectsrelocalizationchanging,
      title={Dynamic Objects Relocalization in Changing Environments with Flow Matching}, 
      author={Francesco Argenziano and Miguel Saavedra-Ruiz and Sacha Morin and Daniele Nardi and Liam Paull},
      year={2025},
      eprint={2509.16398},
      archivePrefix={arXiv},
      primaryClass={cs.RO},
      url={https://arxiv.org/abs/2509.16398}, 
}

This repository is released under the MIT License. See LICENSE for details.