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soft_robot_DEnKF

This repository is the official implementation of the paper "Learning Soft Robot Dynamics using Differentiable Kalman Filters and Spatio-Temporal Embeddings", which has been accepted to 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2023)

Getting Started

1. docker workflow

Clone the repo git clone https://github.com/ir-lab/soft_robot_DEnKF.git and then set the environment variables. Edit the conf.sh file to set the environment variables used to start the docker containers.

IMAGE_TAG=  # unique tag to be used for the docker image.
CONTAINER_NAME=UR5  # name of the docker container.
DATASET_PATH=/home/xiao/datasets/  # Dataset path on the host machine.
CUDA_VISIBLE_DEVICES=0  # comma-separated list of GPU's to set visible.

Build the docker image by running ./build.sh.

2. Training or testing - PyTorch

Create or a modify a yaml file found in ./pyTorch/config/tensegrity_xx.yaml with the appropriate parameters. Set the mode parameter to perform the training or testing routine.

mode:
    mode: 'train'  # 'train' | 'test'

Run the training and test script using the bash file ./run_filter.sh $CONFIG_FILE where $CONFIG_FILE is the path to the config file. e.g. ./run_filter.sh ./config/tensegrity_xx.yaml. View the logs with docker logs -f $CONTAINER_NAME

3. Tensorboard

Use the docker logs to copy the tensorboard link to a browser

docker logs -f $CONTAINER_NAME-tensorboard

4. Without Docker

If you don't want to use the docker container for training, you may directly use the train.py script and pass in the config file. Make sure to have corresponding libraries and dependencies installed on your local machine. Plase refer to requirement.txt and Dockerfile for those required packages. Go to ./soft_robot and then Run python train.py --config ./config/tensegrity_xx.yaml

Models

differentiable Ensemble Kalman Filters

This project introduces a novel differentiable filter called differentiable Ensemble Kalman Filters (DEnKF), for modeling soft robots. It offers an end-to-end learning approach to estimate the state of the system, which is highly nonlinear and difficult to model analytically. The main contributions are:

  • The introduction of a positional embedding process that enables the spatial generalization of DEnKF by encoding observations with respect to their positions. As a result, learned models can account for changes to the location of sensors on the robot body.
  • The use of a temporal embedding process that allows DEnKF to perform inference at variable rates and account for a multitude of time-delays due to sensing, hardware or communication.
  • The modular structure of the framework separates the state dynamics from the end-to-end learning process, ensuring that the state remains dynamic even in the absence of observations.
  • The paper also demonstrates a downstream application task of the of the framework for estimating human contact and physical interactions with the robot.

Datasets

Tensegrity_dataset

The Dataset is available upon request. (Dr. Ikemoto: ikemoto@brain.kyutech.ac.jp)

Results

Real-time Tracking

The real-time estimation of the state on the tensegrity robot arm is demonstrated in this study. Specifically, the `left` figure presents a motion sequence of the robot without applying external forces, while the `right` figure showcases the real-time tracking outcomes (along with the corresponding uncertainty) of the positions of the hand tip.

Comparison

Comparison with other baselines on state estimation task measured in RMSE and MAE of the EE position with fixed IMU locations Z. Results for dEKF, dPF, and dPF-M-lrn are reproduced for detailed comparisons.

Method RMSE MAE (mm) Wall clock time (s)
dEKF 61.753±1.630 41.960±1.147 0.047
DPF 51.184±7.204 34.317±4.205 0.060
dPF-M-lrn 49.799±8.264 33.903±6.964 0.059
DEnKF 31.519±9.974 25.777±7.827 0.062

Citation

  • Please cite the paper if you used any materials from this repo, Thanks.
@article{liu2023learning,
  title={Learning Soft Robot Dynamics using Differentiable Kalman Filters and Spatio-Temporal Embeddings},
  author={Liu, Xiao and Ikemoto, Shuhei and Yoshimitsu, Yuhei and Amor, Heni Ben},
  journal={arXiv preprint arXiv:2308.09868},
  year={2023}
}