PyRoki: A Modular Toolkit for Robot Kinematic Optimization
Chung Min Kim, Brent Yi, Hongsuk Choi, Yi Ma, Ken Goldberg, Angjoo Kanazawa
Abstract
Robot motion can have many goals. Depending on the task, we might optimize for pose error, speed, collision, or similarity to a human demonstration. Motivated by this, we present PyRoki: a modular, extensible, and deviceagnostic toolkit for solving kinematic optimization problems. PyRoki couples an interface for specifying kinematic variables and costs with an efficient nonlinear least squares optimizer. Unlike existing tools, it is also device-agnostic: optimization runs natively on CPU, GPU, and TPU. In this paper, we present (i) the design and implementation of PyRoki, (ii) motion retargeting and planning case studies that highlight the advantages of PyRoki’s modularity, and (iii) optimization benchmarking, where PyRoki can be 1.4-1.7x faster and converges to lower errors than cuRobo, an existing GPU-accelerated inverse kinematics library. The code is open-sourced at https://pyroki-toolkit.github.io.
BibTeX
@inproceedings{iros2025_pyrokiamodularto,
title = {PyRoki: A Modular Toolkit for Robot Kinematic Optimization},
author = {Chung Min Kim and Brent Yi and Hongsuk Choi and Yi Ma and Ken Goldberg and Angjoo Kanazawa},
booktitle = {IROS 2025},
year = {2025}
}