Object pose estimation
using multiple vision-based tactile sensors

Collision-aware In-hand 6D Object Pose Estimation using Multiple Vision-based Tactile Sensors

2023 IEEE International Conference on Robotics and Automation (ICRA), London, United Kingdom, 2023, pp. 719-725

Paper | Video

Update
Installation
Citing this paper

Update

final repo merged 1/03/24
missing dataset--> to be added soon

⚙️ Installation

Strict Requirements

pytorch

pip install torch==1.10.1+cu111 torchvision==0.11.2+cu111 torchaudio==0.10.1 -f https://download.pytorch.org/whl/cu111/torch_stable.html

pip install jaxlie jax==0.3.4

jaxlib

pip install https://storage.googleapis.com/jax-releases/cuda11/jaxlib-0.3.2+cuda11.cudnn82-cp38-none-manylinux2010_x86_64.whl

NVIDIA PhysX SDK 4.1

Dependencies

numpy==1.22.3
scipy==1.8.0
pyquaternion
tqdm

How to install

No docker

1. Install NVIDIA PhysX SDK 4.1

Clone the repo and follow the instructions. For linux installation, you need to install clang:

sudo apt-get install clang

In order to let CMAKE use it as compiler, you should export the CMAKE_CXX_COMPILER environment variable in this way:

export CMAKE_CXX_COMPILER=/usr/bin/clang

sudo apt-get install libxxf86vm-dev

2. Clone repository

git clone https://github.com/hsp-iit/multi-tactile-6d-estimation.git 
cd multi-tactile-6d-estimation

Then, you need to compile the collision detection code.

cd physX 
mkdir build

To compile the code, you need to set in the CMakeLists.txt file the absolute path to PhysX/physx you compiled in the first step.

cd build
cmake .. -DCMAKE_BUILD_TYPE=Debug "-GUnix Makefiles" -DCMAKE_CXX_FLAGS=-Wno-restrict -Wno-class-memaccess 
sed -e s/-Werror//g -i /home/user/multi-tactile-6d-estimation/physX/build/externals/physx/sdk_source_bin/CMakeFiles/PhysXExtensions.dir/flags.make
make

3. Get the model

cd multi-tactile-6d-estimation
apt install git-lfs
git lfs install
git clone https://huggingface.co/gabrielecaddeo/tactile-autoencoder

Docker

You need to build the image provided in docker folder by running

cd docker
bash run.sh

How to run

To reproduce the experiments, you need to run

cd multi-tactile-6d-estimation/optimization
bash run.sh /path/to/multi-tactile-6d-estimation /path/to/multi-tactile-6d-estimation/tactile-autoencoder/weights/model_real_back_norm.pth

You will find the table_all_objects.tex in results_directory_pose_estimation directory

Fix

The rotation metrics' results are expected to be slightly better than those presented in the paper, attributed to a minor error in the final_results.py file

📰 Citing this paper

@INPROCEEDINGS{10160359,
  author={Caddeo, Gabriele M. and Piga, Nicola A. and Bottarel, Fabrizio and Natale, Lorenzo},
  booktitle={2023 IEEE International Conference on Robotics and Automation (ICRA)}, 
  title={Collision-aware In-hand 6D Object Pose Estimation using Multiple Vision-based Tactile Sensors}, 
  year={2023},
  volume={},
  number={},
  pages={719-725},
  doi={10.1109/ICRA48891.2023.10160359}}

🧔 Maintainer

This repository is maintained by:


	@gabrielecaddeo

hsp-iit/multi-tactile-6d-estimation

Object pose estimation using multiple vision-based tactile sensors