Sequential Latent Temporal model for Predicting Routine Object Usage (SLaTe-PRO)

SLaTe-PRO, presented in paper Predicting Routine Object Usage for Proactive Robot Assistance learns a shared latent space across observation domains to represent user's routine behavior and perform predictions. It is composed of autoencoders to encode each observation domain into the latent space and a recurrent model to perform predictions in this space.

This repository includes

• SLaTe-PRO model and training code
• HOMER+ dataset, which is based on the HOMER dataset
• Checkpoints of SLaTe-PRO trained on the above HOMER+ dataset

How-Tos

To run a previously trained model, run the below code for one of HOuseholdA, HouseholdB or HouseholdC

python ./run.py --activity_availability=100 --path=./data/HOMER+/householdA --logs_dir=./logs --ckpt_dir=./checkpoints/HouseholdA/default_100 --read_ckpt

To train a model, run the below code for one of HOuseholdA, HouseholdB or HouseholdC

python ./run.py --activity_availability=100 --path=./data/HOMER+/householdA --logs_dir=./logs

Citation

If this work proved helpful, consider citing it as:

@inproceedings{patel2023predicting,
  title={Predicting Routine Object Usage for Proactive Robot Assistance},
  author={Patel, Maithili and Prakash, Aswin and Chernova, Sonia},
  booktitle={7th Annual Conference on Robot Learning},
  year={2023}
}