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<h1 align="center"> HoRoPose: Real-time Holistic Robot Pose Estimation with Unknown States <br> (ECCV 2024)</h1> <div align="center">

</div> <img src="assets/holistic.gif" width="800"/>

This is the official PyTorch implementation of the paper "Real-time Holistic Robot Pose Estimation with Unknown States". It provides an efficient framework for <b>real-time</b> robot pose estimation from RGB images <b>without</b> requiring known robot states.

Installation

This project's dependencies include python 3.9, pytorch 1.13, pytorch3d 0.7.4 and CUDA 11.7. The code is developed and tested on Ubuntu 20.04.

    pip install torch==1.13.1+cu117 torchvision==0.14.1+cu117
    pip install -r requirements.txt
    conda install pytorch3d=0.7.4 # from https://anaconda.org/pytorch3d/pytorch3d/files

Data and Model Preparation

In our work, we use the following data and pretrained model:

The DREAM datasets consisting of both real and synthetic subsets, placed under ${ROOT}/data/dream/$ .
The URDF (Unified Robotics Description Format) of robot Panda, Kuka and Baxter, placed under ${ROOT}/data/deps/$ .
The pretrained HRnet backbone for pose estimation, placed under ${ROOT}/models/$ .
The openly available foreground segmentation model of 4 real datasets of Panda from CtRNet, placed under ${ROOT}/models/panda_segmentation/$ .

You can download the data and models through provided links. When finished, the directory tree should look like this.

${ROOT}
|-- data
    |-- dream
    |   |-- real
    |   |   |-- panda-3cam_azure  
    |   |   |-- panda-3cam_kinect360
    |   |   |-- panda-3cam_realsense
    |   |   |-- panda-orb
    |   |-- synthetic
    |   |   |-- baxter_synth_test_dr
    |   |   |-- baxter_synth_train_dr
    |   |   |-- kuka_synth_test_dr
    |   |   |-- kuka_synth_test_photo
    |   |   |-- kuka_synth_train_dr
    |   |   |-- panda_synth_test_dr
    |   |   |-- panda_synth_test_photo
    |   |   |-- panda_synth_train_dr
    |-- deps
    |   |-- baxter-description
    |   |-- kuka-description
    |   |-- panda-description
|-- models
    |-- panda_segmentation
    |   |-- azure.pth
    |   |-- kinect.pth
    |   |-- orb.pth
    |   |-- realsense.pth
    |-- hrnet_w32-36af842e_roc.pth

Train

We train our final model in a multi-stage fashion. All model is trained using a single NVIDIA V100 with 32GB GPU. Distributed training is also supported.

We use config files in configs/ to specify the training process. We recommend filling in the exp_name field in the config files with a unique name, as the checkpoints and event logs produced during training will be saved under experiments/{exp_name}. The correspondent config file will be automatically copied into this directory.

Synthetic Datasets

Firstly, pretrain the depthnet (root depth estimator) for 100 epochs for each robot arm:

python scripts/train.py --config configs/panda/depthnet.yaml
python scripts/train.py --config configs/kuka/depthnet.yaml
python scripts/train.py --config configs/baxter/depthnet.yaml

With depthnet pretrained, we can train the full network for 100 epochs:

python scripts/train.py --config configs/panda/full.yaml
python scripts/train.py --config configs/kuka/full.yaml
python scripts/train.py --config configs/baxter/full.yaml

To save your time when reproducing results of our paper, we provide readily-pretrained depthnet model weights for full network training. To use them, you can modify the configs/{robot}/full.yaml file by filling in the pretrained_rootnet field with the path of the downloaded .pk file.

Real Datasets of Panda

We employ self-supervised training for the 4 real datasets of Panda.

Firstly, train the model on synthetic dataset using configs/panda/self_supervised/synth.yaml for 100 epochs. Be sure to fill in the pretrained_rootnet field with the path of the pretrained Panda depthnet weight in advance.

python scripts/train.py --config configs/panda/self_supervised/synth.yaml

The training process above saves checkpoints for 4 real datasets for further self-supervised training (e.g. experiments/{exp_name}/ckpt/curr_best_auc(add)_azure_model.pk).

When finished training on synthetic data, modify the configs/panda/self_supervised/{real_dataset}.yaml file by filling in the pretrained_weight_on_synth field with the path of the correspondent checkpoint. Then start self-supervised training with:

python scripts/train.py --config configs/panda/self_supervised/azure.yaml
python scripts/train.py --config configs/panda/self_supervised/kinect.yaml
python scripts/train.py --config configs/panda/self_supervised/realsense.yaml
python scripts/train.py --config configs/panda/self_supervised/orb.yaml

Test

To evaluate models, simply run:

python scripts/test.py --exp_path {path of the experiment folder} --dataset {dataset name}
# e.g. python scripts/test.py -e experiments/panda_full --dataset panda_synth_test_dr

Note that each model is presented in a folder containing ckpt/, log/ and config.yaml. After running test script, result/ will be generated inside the folder.

Model Zoo

You can download our final models from Google Drive and evaluate them yourself.

Citation

If you use our code or models in your research, please cite with:

@inproceedings{holisticrobotpose,
  author={Ban, Shikun and Fan, Juling and Ma, Xiaoxuan and Zhu, Wentao and Qiao, Yu and Wang, Yizhou},
  title={Real-time Holistic Robot Pose Estimation with Unknown States},
  booktitle = {European Conference on Computer Vision (ECCV)},
  year = {2024}
}

Acknowledgment

This repo is built on the excellent work RoboPose and CtRNet. Thank the authors for releasing their codes.