SpatioTemporalObjectTracking

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SpatioTemporalObjectTracking

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Graph translation network for modeling dynamic object semantics

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# SpatioTemporalObjectTracking
Graph translation network for modeling spatio-temporal dynamics of household objects for our paper ['Proactive Robot Assistance via Spatio-Temporal Object Modeling'](https://openreview.net/pdf?id=th7GW868Pok). The model reads in an input graph representing the environment and time, and translates it to a probabilistic output graph representing the environment at the next time step. It uses the HOMER dataset, details about which can be found [here](https://github.com/GT-RAIL/rail_tasksim/tree/homer/routines). The processed version of the dataset required to run this code is present in this repository, however the complete dataset, containing human readable scripts, object arrangement trees, visuals, etc., is available for download [here](https://www.dropbox.com/s/8qs1znw3fmqho44/HOMER.zip?dl=0)

### Running this model
To run the model on the existing dataset, you can use the `run.py` with the path to the dataset and config file. e.g. `python3 ./run.py --path=$dataset`.

Some common things to do are:
- Running the model as is on a dataset
     `python3 ./run.py --path=$dataset --name=ours --train_days=$train_days --logs_dir=$logs_dir/$train_days --write_ckpt`
- Running the baselines on a dataset
     `python3 ./run.py --cfg=default --path=$dataset --baselines --train_days=$train_days --logs_dir=$logs_dir/$train_days`
- Pretrain a model on a combination of datasets
     `python3 ./run_pt.py --path=$d_target --pretrain_dirs=$d0,$d1,$d2,$d3,$d4 --name=ourspt --train_days=$logs_dir/$train_days --logs_dir=$logs_dir/$train_days --write_ckpt`
- Finetune a pretrained model
     `python3 ./run.py --path=$d_target --name=ourspt --train_days=$train_days --logs_dir=$logs_dir/$train_days --write_ckpt --finetune --ckpt_dir=$ckpt_root/0/$target/ourspt`
- Run ablations or other configurations
     `python3 ./run.py --cfg=$myNewConfig --path=$dataset --name=$myNewConfig --train_days=30 --logs_dir=$logs_dir/$train_days --write_ckpt`

A processed version of the [HOMER dataset](https://github.com/GT-RAIL/rail_tasksim/tree/homer/routines) used for the results is present in the `data/` directory of this repository, and can be used directly with the model using the above commands. In order to use a different dataset generated using HOMER, first copy the dataset into `data/` directory, and then run `helpers/reader.py` with appropriate arguments to run the necessary pre-processing. This needs to be done only once.

If you're curious about the code itself:
- The model and it's helper functions can be found in `GraphTranslatorModule.py`
- The `reader.py` file contains code to process the (HOMER) dataset
- The evaluation functions for our model are in `evaluations.py`

### Citation
```
@inproceedings{patel2022proactive,
  title={Proactive Robot Assistance via Spatio-Temporal Object Modeling},
  author={Patel, Maithili and Chernova, Sonia},
  booktitle={6th Annual Conference on Robot Learning},
  year={2022}
}
```

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