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Computer Science > Robotics

arXiv:2505.03728 (cs)
[Submitted on 6 May 2025]

Title:PyRoki: A Modular Toolkit for Robot Kinematic Optimization

Authors:Chung Min Kim, Brent Yi, Hongsuk Choi, Yi Ma, Ken Goldberg, Angjoo Kanazawa
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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 cross-platform 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 cross-platform: 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.
Comments: First two authors contributed equally. Code is available at this https URL
Subjects: Robotics (cs.RO)
Cite as: arXiv:2505.03728 [cs.RO]
  (or arXiv:2505.03728v1 [cs.RO] for this version)
  https://doi.org/10.48550/arXiv.2505.03728

Submission history

From: Chung Min Kim [view email]
[v1] Tue, 6 May 2025 17:56:40 UTC (2,656 KB)
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