Hidden Parameter Recurrent State Space Models (HiP-RSSM)
Pytorch code for ICLR 2022 paper Hidden Parameter Recurrent State Space Models For Changing Dynamics Scenarios. The algorithm learns deep multi task Kalman Filters that can be used in non-stationary environments with changing dynamics.
- torch==1.3.1
- python 3.7
- omegaconf==2.1.1
- hydra-core==1.1.1
- PyYAML==5.3
- wandb==0.10.25
- umap-learn
With HiP-RSSM
as the working directory execute the python script
python experiments/mobileRobot/mobile_robot_hiprssm.py model=default
The dataset used here is that of a mobile robot traversing terrain of different slopes as reported in the paper.
For any dataset with a long timeseries, split them to reasonable local trajectories of length L=2*K, which is fed into the hiprssm model. The first K would used by context encoder to infer latent context and the last K would be used as target set. The concept is very similar to context sets and target sets in Neural Processes or the meta testing procedure used in this reference.
A detailed description for creating training datasets is given in Appendix E. A detailed description for testtime inference procedure is given in Algorithm 1 in the appendix.
With HiP-RSSM
as the working directory execute the python script
- LSTM Baseline:
python experiments/mobileRobot/mobile_robot_rnn.py model=default_lstm
- GRU Baseline:
python experiments/mobileRobot/mobile_robot_rnn.py model=default_gru
- RKN Baseline:
python experiments/mobileRobot/mobile_robot_hiprssm.py model=default_rkn