HJCD-IK (Hybrid Jacobian Coordinate Descent)

A GPU-accelerated inverse kinematics solver built on GRiD.

Requirements

• NVIDIA GPU + CUDA Toolkit 12.x
• Python ≥ 3.9
• CMake ≥ 3.23
• Visual Studio 2022 (Windows) or GCC/Clang (Linux)

Clone

git clone --recurse-submodules https://github.com/A2R-Lab/HJCD-IK.git
cd HJCD-IK

Install the Python package

You can install hjcdik with pip on Python ≥ 3.9:

python -m pip install -U pip
python -m pip install -e .

Initial Start

After installation, configure an initial GRiD header file for the robot:

cd external/GRiD
python generateGRiD.py /path/to/urdf
cd ../..

For testing we provide panda and fetch urdf files in include/test_urdf.

Benchmark

Once initializing a GRiD header file, run:

python benchmarks/ik_benchmark.py --skip-grid-codegen

To run the Panda Arm benchmark on batch sizes of 1,10,100,1000,2000. Results are written to a results.yml file.

Usage

• --num-targets <int>
  • How many target poses to sample. Default: 100
• --batches "<list>"
  • Batch sizes to test (comma or space separated). Default: "1,10,100,1000,2000"
• --num-solutions <int>
  • How many IK solutions to return per call. Default: 1
• --yaml-out <path>
  • Output result file. Default: results.yml
• --urdf <path>
  • URDF path used if running GRiD codegen. Default: include/test_urdf/panda.urdf
• --skip-grid-codegen
  • Skips creating GRiD header file and immediately runs benchmarks. Default: off
• --seed <int>
  • Seed for target sampling. Default: 0

Usage Examples

• Custom batches/targets/solutions, write a custom file:

python benchmarks/ik_benchmark.py \
  --batches "1,32,256,2048" \
  --num-targets 250 \
  --num-solutions 4 \
  --yaml-out results.yml \ 
  --skip-grid-codegen

• Run with GRiD code-gen using specific URDF:

python benchmarks/ik_benchmark.py --urdf include/test_urdf/panda.urdf