HJCD-IK: Hybrid Jacobian Coordinate Descent Inverse Kinematics

This repository contains the code from "HJCD-IK: GPU-Accelerated Inverse Kinematics through Batched Hybrid Jacobian Coordinate Descent"

Requirements

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

Installation

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

HJCD-IK relies on GRiD, a GPU-accelerated library for rigid body dynamics and analytical gradients.

(Mac/Linux)

chmod +x scripts/bootstrap.sh
./scripts/bootstrap.sh

(Windows)

.\scripts\bootstrap_windows.bat

You can install hjcdik with pip on Python ≥ 3.9:

python -m pip install -e .

Benchmark

To run IK benchmarks, use:

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

which performs IK using the Panda Arm with batches of 1, 10, 100, 1000, 2000. Results are written to a results.yml.

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, out file name:

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

• To generate a new GRiD header on a different robot, run:

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

Note on custom robots:

HJCD-IK and GRiD currently only support robots using revolute, prismatic, and fixed joints without any closed kinematics loops.

Cite

Please cite HCJD-IK if you found this work useful:

@article{yasutake2025hjcd,
  title={HJCD-IK: GPU-Accelerated Inverse Kinematics through Batched Hybrid Jacobian Coordinate Descent},
  author={Yasutake, Cael and Kingston, Zachary and Plancher, Brian},
  journal={arXiv preprint arXiv:2510.07514},
  year={2025}
}