Smart grasping for 2F grippers

20.05.2026 - Successfully replicated https://github.com/mousecpn/Implicit-Grasp-Diffusion with newer libraries running on RTX 5090

Prerequisites

OS / Platform:            Linux-6.8.1-1048-realtime-x86_64-with-glibc2.39 (Ubuntu 24.04 and ROS2 Jazzy (not used yet))
Linux Kernel:             6.8.1-1048-realtime
Python Version:           3.11.15

PyTorch:                  2.11.0+cu128
CUDA Available:           True
CUDA Device Name:         NVIDIA GeForce RTX 5090
Numpy:                    1.26.4
Scipy:                    1.17.1
Trimesh:                  4.12.2
Open3d:                   0.19.0

pybullet build time: Jan 29 2025 23:17:20
PyBullet:                 3.2.7
URDFpy:                   0.0.4 (Patched)

1. conda create -n [ENV_NAME] python=3.11 -y
2. Install pytorch and CUDA.
3. Install packages list in requirements.txt. Then install torch-scatter following here, based on pytorch version and cuda version. (PS: if there is an error about sklearn when installing open3d, you can export SKLEARN_ALLOW_DEPRECATED_SKLEARN_PACKAGE_INSTALL=True)
4. Go to the root directory and install the project locally using pip pip install -e .
5. Build ConvONets dependents by running python scripts/convonet_setup.py build_ext --inplace.
6. cd [PROJECT ROOT]
7. mkdir data
8. download data: https://utexas.app.box.com/s/h3ferwjhuzy6ja8bzcm3nu9xq1wkn94s and https://github.com/UT-Austin-RPL/GIGA#pre-trained-models-and-pre-generated-data
9. Check additional documents in README-Gizem.md. Make sure that your data folder has

  .
  ├── experiments
  ├── models
  ├── packed
  ├── pile
  └── urdfs

10. Export the zip files in pile/ and packed/

How to run

export PYTHONPATH=$(pwd)/src:$PYTHONPATH
python scripts/sim_grasp_multiple.py \
  --num-view 1 --object-set pile/test --scene pile \
  --num-rounds 5 \
  --sideview \
  --add-noise dex \
  --force --best \
  --model ./data/models/IGD_pile.pt \
  --type igd \
  --result-path results/debug_visual \
  --vis \
  --sim-gui

(remove --vis and --sim-gui for headless mode)